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Next-generation sequencing recently revealed that recurrent disruptive mutations in a few genes may account for 1% of sporadic autism cases. Coupling these novel genetic data to empirical assays of protein function can illuminate crucial molecular networks. Here we demonstrate the power of the approach, performing the first functional analyses of TBR1 variants identified in sporadic autism. De novo truncating and missense mutations disrupt multiple aspects of TBR1 function, including subcellular localization, interactions with co-regulators and transcriptional repression. Missense mutations inherited from unaffected parents did not disturb function in our assays. We show that TBR1 homodimerizes, that it interacts with FOXP2, a transcription factor implicated in speech/language disorders, and that this interaction is disrupted by pathogenic mutations affecting either protein. These findings support the hypothesis that de novo mutations in sporadic autism have severe functional consequences. Moreover, they uncover neurogenetic mechanisms that bridge different neurodevelopmental disorders involving language deficits. Autism spectrum disorders (ASD) are characterized by social impairments, communication deficits and repetitive stereotyped behaviours. Here, the authors show that de novo missense mutations, but not inherited missense mutations, in TBR1 disrupt the protein function and contribute to ASD aetiology.

Background: The US population is aging and has an expanding set of healthcare needs for the prevention and management of chronic conditions. Older adults contribute disproportionately to US healthcare costs, accounting for 34% of total healthcare expenditures in 2014 but only 15% of the population. Fully automated, digital health programs offer a scalable and cost-effective option to help manage chronic conditions. However, the literature on technology use suggests that older adults face barriers to the use of digital technologies that could limit their engagement with digital health programs. The objective of this study was to characterize the engagement of adults 65 years and older with a fully automated digital health platform called Lark Health and compare their engagement to that of adults aged 35–64 years. Methods: We analyzed data from 2,169 Lark platform users across four different coaching programs (diabetes prevention, diabetes care, hypertension care, and prevention) over a 12-month period. We characterized user engagement as participation in digital coaching conversations, meals logged, and device measurements. We compared engagement metrics between older and younger adults using nonparametric bivariate analyses. Main Results: Aggregate engagement across all users during the 12-month period included 1,623,178 coaching conversations, 588,436 meals logged, and 203,693 device measurements. We found that older adults were significantly more engaged with the digital platform than younger adults, evidenced by older adults participating in a larger median number of coaching conversations (514 vs. 428) and logging more meals (174 vs. 89) and device measurements (39 vs. 28) all p ≤ 0.01. Conclusions: Older adult users of a commercially available, fully digital health platform exhibited greater engagement than younger adults. These findings suggest that despite potential barriers, older adults readily adopted digital health technologies. Fully digital health programs may present a widely scalable and cost-effective alternative to traditional telehealth models that still require costly touchpoints with human care providers.

Objective The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC). Methods We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n  =  191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n  =  223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members. Results CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M  =  5.3%, SE  =  .8 vs. M  =  3.3%, SE  =  .8; p  =  .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE  =  .1), with 35% achieving ≥5%. Male sex (β = .11; P  =  .009), weeks with ≥2 weigh-ins (β = .68; P < .0001), and days with an AI-powered coaching exchange (β = .43; P < .0001) were associated with a greater likelihood of achieving ≥5% weight loss. Conclusions An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.

As of March 2021, in the USA, the COVID-19 pandemic has resulted in over 500,000 deaths, with a majority being people over 65 years of age. Since the start of the pandemic in March 2020, preventive measures, including lockdowns, social isolation, quarantine, and social distancing, have been implemented to reduce viral spread. These measures, while effective for risk prevention, may contribute to increased social isolation and loneliness among older adults and negatively impact their mental and physical health. This study aimed to assess the impact of the COVID-19 pandemic and the resulting \"Stay-at-Home\" order on the mental and physical health of older adults and to explore ways to safely increase social connectedness among them. This qualitative study involved older adults living in a Continued Care Senior Housing Community (CCSHC) in southern California, USA. Four 90-minute focus groups were convened using the Zoom Video Communications platform during May 2020, involving 21 CCSHC residents. Participants were asked to describe how they were managing during the \"stay-at-home\" mandate that was implemented in March 2020, including its impact on their physical and mental health. Transcripts of each focus group were analyzed using qualitative methods. Four themes emerged from the qualitative data: (1) impact of the quarantine on health and well-being, (2) communication innovation and technology use, (3) effective ways of coping with the quarantine, and (4) improving access to technology and training. Participants reported a threat to their mental and physical health directly tied to the quarantine and exacerbated by social isolation and decreased physical activity. Technology was identified as a lifeline for many who are socially isolated from their friends and family. Our study findings suggest that technology access, connectivity, and literacy are potential game-changers to supporting the mental and physical health of older adults and must be prioritized for future research.

Background Past studies have utilized external interfaces like resistive bands and motor-generated pulling systems to increase limb propulsion during walking on a motorized treadmill. However, assessing changes in limb propulsion against increasing resistance demands during self-controlled walking has not been undertaken. Purpose We assessed limb propulsion against increasing fore-aft loading demands by applying graded fore-aft (FA) resistance at the center of mass during walking in a novel, intent-driven treadmill environment that allowed participants to control their walking speeds. We hypothesized that to maintain a target speed against progressively increasing resistance, participants would proportionately increase their limb propulsion without increasing vertical force production, with accompanying increases in trailing limb angle and positive joint work. Methods Seventeen healthy-nonimpaired participants (mean age 52 yrs., SD = 11) walked at a target, self-controlled speed of 1.0 m/s against 10, 15, 20, and 25% (% body weight) FA resistance levels. We primarily assessed linear slope values across FA resistance levels for mean propulsive force and impulse and vertical impulse of the dominant limb using one-sample t -tests. We further assessed changes in trailing and leading limb angles and joint work using one-way ANOVAs. Results Participants maintained their target velocity within an a priori defined acceptable range of 1.0 m/s ± 0.2. They significantly increased propulsion proportional to FA resistance (propulsive force mean slope = 2.45, SD = 0.7, t (16) =14.44, p  < 0.01; and propulsive impulse mean slope = 0.7, SD = 0.25, t (16) = 11.84, p  < 0.01), but had no changes in vertical impulse (mean slope = − 0.04, SD =0.17, p  > 0.05) across FA resistance levels. Mean trailing limb angle increased from 24.3° at 10% resistance to 27.4° at 25% ( p  < 0.05); leading limb angle decreased from − 18.4° to − 12.6° ( p  < 0.05). We also observed increases in total positive limb work (F (1.7, 26) = 16.88, p  ≤ 0.001, η 2  = 0.5), primarily attributed to the hip and ankle joints. Conclusions FA resistance applied during self-driven walking resulted in increased propulsive-force output of healthy-nonimpaired individuals with accompanying biomechanical changes that facilitated greater limb propulsion. Future rehabilitation interventions for neurological populations may be able to utilize this principle to design task-specific interventions like progressive strength training and workload manipulation during aerobic training for improving walking function.

Lifestyle modification programs play a critical role in preventing and managing cardiovascular disease (CVD). A key aim of many programs is improving patients' self-efficacy. In-person lifestyle modification programs can enhance self-efficacy in managing CVD risk, also known as cardiac self-efficacy (CSE). However, such programs are typically staffing and resource intensive. Digital lifestyle modification programs may offer a scalable and accessible way to improve CSE, but this has not been shown in prior research. This study examined changes in CSE among individuals using a digital lifestyle modification program for cardiovascular health. Evaluation of improvement in CSE was a secondary goal of a feasibility and acceptability pilot study of a digital program for Heart Health. Participants were individuals with elevated risk for CVD who enrolled in a 90-day pilot study that involved mobile app-based, artificial intelligence-powered health coaching and educational lessons focused on behaviors that promote cardiovascular health. Participants completed the 9-item CSE Scale at baseline and in month 2. Changes in confidence in participants' ability to manage their cardiovascular health were assessed. The sample included 273 (n=207, 61.2% female; mean age 59.3, SD 10.1 years) participants who submitted a complete CSE Scale at baseline and in month 2. The total CSE Scale score increased by 12.9% (P<.001) from baseline to month 2. Additionally, there were significant increases in mean score on each of the 9 individual CSE Scale items (all P<.001), with the largest increases in confidence \"in knowing when to call or visit the doctor for your heart disease\" (17% increase; P<.001), \"in knowing how much physical activity is good for you\" (16.3% increase; P<.001), and \"that you can get regular aerobic exercise\" (19% increase; P<.001). The present analyses indicate that participants in a digital lifestyle modification program for cardiovascular health showed significant improvements in CSE within 2 months. This work adds to the growing literature examining ways to improve health-related self-efficacy and scalable access to programs for prevention and management of CVD.

Background Treadmill training, with or without body-weight support (BWSTT), typically involves high step count, faster walking speed, and higher heart-rate intensity than overground walking training. The addition of challenging mobility skill practice may offer increased opportunities to improve walking and balance skills. Here we compare walking and balance outcomes of chronic stroke survivors performing BWSTT with BWSTT including challenging mobility skills. Methods Single-blind randomized clinical trial comparing two BWSTT interventions performed in a rehabilitation research laboratory facility over 6 weeks. Participants were 18+ years of age with chronic (≥5 months) poststroke hemiparesis due to a cortical or subcortical ischemic or hemorrhagic stroke and walking speeds < 1.1 m/s at baseline. A hands-free group (HF; n  = 15) performed BWSTT without assistance from handrails or assistive devices, and a hands-free plus challenge group (HF + C; n  = 14) performed the same protocol while additionally practicing challenging mobility skills. The primary outcome was change in comfortable walking speed (CWS), with secondary outcomes of fast walk speed (FWS), six-minute walk distance, Berg Balance Scale (BBS) scores, and Activities Specific Balance Confidence (ABC) scores. Results Significant pre-post improvement of CWS ( Z = − 4.2, p ≤ 0.0001 ) from a median of 0.35 m/s (range 0.10 to 1.09) to a median of 0.54 m/s (range 0.1 to 1.17), but no difference observed between groups ( U = 96.0, p = 0.69 ). Pre-post improvements across all participants resulted in reclassified baseline ambulation status from sixteen to ten household ambulators, three to seven limited community ambulators, and ten to twelve community ambulators. Secondary outcomes showed similar pre-post improvements with no between-group differences. Conclusions The addition of challenging mobility skills to a hands-free BWSTT protocol did not lead to greater improvements in CWS following 6 weeks of training. One reason for lack of group differences may be that both groups were adequately challenged by walking in an active, self-driven treadmill environment without use of handrails or assistive devices. Trial registration NCT02787759 Falls-based Training for Walking Post-Stroke (FBT); retrospectively registered June 1st, 2016.

Digital health programs have been shown to be feasible and effective for the prevention of chronic diseases such as diabetes. Contrary to expectations, findings also suggest that older adults have higher levels of engagement with digital health programs than younger adults. However, there is a paucity of research examining outcomes among older adults in digital health programs and whether higher engagement is related to better outcomes. We examined weight loss outcomes for 538 users aged 65 and older participating in one of two app-based prevention programs called the Diabetes Prevention Program and the Prevention Program, respectively. Both programs were available on a single artificial intelligence (AI)-powered digital health platform and shared a common goal of weight loss. We also examined the relationship between key engagement metrics (i.e., conversing with the AI-powered coach, weigh-ins, and initiating educational lessons early in the program) and weight loss outcomes. The average weight loss of all enrollees having a weight measurement after after the 9th week was 4.51%, and the average weight loss of the Diabetes Prevention Program enrollees meeting a minimum engagement level was 8.56%. Greater weight loss was associated with a greater number of days with AI-powered coaching conversations ( = 0.03), more weigh-ins ( = 0.00), and early educational lesson initiation ( = 0.02). Digital health programs powered by AI offer a promising solution for health management among older adults. The results show positive health outcomes using app-based prevention programs, and all three engagement metrics were independently associated with weight loss.

Cardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits such as on-demand support, lower cost, and increased scalability. Prior research has shown the effectiveness of digital health interventions in reducing negative CVD outcomes. This pilot study focuses on the Lark Heart Health program, a fully digital artificial intelligence (AI)-powered smartphone app, providing synchronous CVD risk counseling, educational content, and personalized coaching. This pilot study evaluated the feasibility and acceptability of a fully digital AI-powered lifestyle change program called Lark Heart Health. Primary analyses assessed (1) participant satisfaction, (2) engagement with the program, and (3) the submission of health screeners. Secondary analyses were conducted to evaluate weight loss outcomes, given that a major focus of the Heart Health program is weight management. This study enrolled 509 participants in the 90-day real-world single-arm pilot study of the Heart Health app. Participants engaged with the app by participating in coaching conversations, logging meals, tracking weight, and completing educational lessons. The study outcomes included participant satisfaction, app engagement, the completion of screeners, and weight loss. On average, Heart Health study participants were aged 60.9 (SD 10.3; range 40-75) years, with average BMI indicating class I obesity. Of the 509 participants, 489 (96.1%) stayed enrolled until the end of the study (dropout rate: 3.9%). Study retention, based on providing a weight measurement during month 3, was 80% (407/509; 95% CI 76.2%-83.4%). Participant satisfaction scores indicated high satisfaction with the overall app experience, with an average score of ≥4 out of 5 for all satisfaction indicators. Participants also showed high engagement with the app, with 83.4% (408/489; 95% CI 80.1%-86.7%) of the sample engaging in ≥5 coaching conversations in month 3. The results indicated that participants were successfully able to submit health screeners within the app, with 90% (440/489; 95% CI 87%-92.5%) submitting all 3 screeners measured in the study. Finally, secondary analyses showed that participants lost weight during the program, with analyses showing an average weight nadir of 3.8% (SD 2.9%; 95% CI 3.5%-4.1%). The study results indicate that participants in this study were satisfied with their experience using the Heart Health app, highly engaged with the app features, and willing and able to complete health screening surveys in the app. These acceptability and feasibility results provide a key first step in the process of evidence generation for a new AI-powered digital program for heart health. Future work can expand these results to test outcomes with a commercial version of the Heart Health app in a diverse real-world sample.

Background:Home blood pressure (BP) monitoring is recommended for people with hypertension; however, meta-analyses have demonstrated that BP improvements are related to additional coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone. High-contact coaching requires substantial resources and may be difficult to deliver via human coaching models.Objective:This observational study assessed changes in BP and body weight following participation in a fully digital program called Lark Hypertension Care with coaching powered by artificial intelligence (AI).Methods:Participants (N=864) had a baseline systolic BP (SBP) ≥120 mm Hg, provided their baseline body weight, and had reached at least their third month in the program. The primary outcome was the change in SBP at 3 and 6 months, with secondary outcomes of change in body weight and associations of changes in SBP and body weight with participant demographics, characteristics, and program engagement.Results:By month 3, there was a significant drop of –5.4 mm Hg (95% CI –6.5 to –4.3; P<.001) in mean SBP from baseline. BP did not change significantly (ie, the SBP drop maintained) from 3 to 6 months for participants who provided readings at both time points (P=.49). Half of the participants achieved a clinically meaningful drop of ≥5 mm Hg by month 3 (178/349, 51.0%) and month 6 (98/199, 49.2%). The magnitude of the drop depended on starting SBP. Participants classified as hypertension stage 2 had the largest mean drop in SBP of –12.4 mm Hg (SE 1.2 mm Hg) by month 3 and –13.0 mm Hg (SE 1.6 mm Hg) by month 6; participants classified as hypertension stage 1 lowered by –5.2 mm Hg (SE 0.8) mm Hg by month 3 and –7.3 mm Hg (SE 1.3 mm Hg) by month 6; participants classified as elevated lowered by –1.1 mm Hg (SE 0.7 mm Hg) by month 3 but did not drop by month 6. Starting SBP (β=.11; P<.001), percent weight change (β=–.36; P=.02), and initial BMI (β=–.56; P<.001) were significantly associated with the likelihood of lowering SBP ≥5 mm Hg by month 3. Percent weight change acted as a mediator of the relationship between program engagement and drop in SBP. The bootstrapped unstandardized indirect effect was –0.0024 (95% CI –0.0052 to 0; P=.002).Conclusions:A hypertension care program with coaching powered by AI was associated with a clinically meaningful reduction in SBP following 3 and 6 months of program participation. Percent weight change was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP. An AI-powered solution may offer a scalable approach to helping individuals with hypertension achieve clinically meaningful reductions in their BP and associated risk of cardiovascular disease and other serious adverse outcomes via healthy lifestyle changes such as weight loss.