Background Type 2 diabetes is the most expensive chronic disease in the United States. Two-thirds of US adults have prediabetes or are overweight and at risk for type 2 diabetes. Intensive in-person behavioral counseling can help patients lose weight and make healthy behavior changes to improve their health outcomes. However, with the shortage of health care providers and associated costs, such programs do not adequately service all patients who could benefit. The health care system needs effective and cost-effective interventions that can lead to positive health outcomes as scale. This study investigated the ability of conversational artificial intelligence (AI), in the form of a standalone, fully automated text-based mobile coaching service, to promote weight loss and other health behaviors related to diabetes prevention. This study also measured user acceptability of AI coaches as alternatives to live health care professionals. Objective The objective of this study was to evaluate weight loss, changes in meal quality, and app acceptability among users of the Lark Weight Loss Health Coach AI (HCAI), with the overarching goal of increasing access to compassionate health care via mobile health. Lessons learned in this study can be applied when planning future clinical trials to evaluate HCAI and when designing AI to promote weight loss, healthy behavior change, and prevention and self-management of chronic diseases. Methods This was a longitudinal observational study among overweight and obese (body mass index ≥25) participants who used HCAI, which encourages weight loss and healthy diet choices through elements of cognitive behavioral therapy. Weight loss, meal quality, physical activity, and sleep data were collected through user input and, for sleep and physical activity, partly through automatic detection by the user’s mobile phone. User engagement was assessed by duration and amount of app use. A 4-question in-app user trust survey assessed app usability and acceptability. Results Data were analyzed for participants (N=70) who met engagement standards set forth by the Centers for Disease Control and Prevention criteria for Diabetes Prevention Program, a clinically proven weight loss program focused on preventing diabetes. Weight loss (standard error of the mean) was 2.38% (0.69%) of baseline weight. The average duration of app use was 15 (SD 1.0) weeks, and users averaged 103 sessions each. Predictors of weight loss included duration of AI use, number of counseling sessions, and number of meals logged. Percentage of healthy meals increased by 31%. The in-app user trust survey had a 100% response rate and positive results, with a satisfaction score of 87 out of 100 and net promoter score of 47. Conclusions This study showed that use of an AI health coach is associated with weight loss comparable to in-person lifestyle interventions. It can also encourage behavior changes and hav...

Interferon (IFN) activates the signal transducer and activator of transcription (STAT) pathway to regulate immune responses. The protein inhibitor of activated STAT (PIAS) family has been suggested to negatively regulate STAT signaling. To understand the physiological function of PIAS1, we generated Pias1(-/-) mice. Using PIAS1-deficient cells, we show that PIAS1 selectively regulates a subset of IFN-gamma- or IFN-beta-inducible genes by interfering with the recruitment of STAT1 to the gene promoter. The antiviral activity of IFN-gamma or IFN-beta was consistently enhanced by Pias1 disruption. Pias1(-/-) mice showed increased protection against pathogenic infection. Our data indicate that PIAS1 is a physiologically important negative regulator of STAT1 and suggest that PIAS1 is critical for the IFN-gamma- or IFN-beta-mediated innate immune responses.

The NF-B family of transcription factors is activated by a wide variety of signals to regulate a spectrum of cellular processes. The proper regulation of NF-B activity is critical, since abnormal NF-B signaling is associated with a number of human illnesses, such as chronic inflammatory diseases and cancer. We report here that PIAS1 ( and bacterial lipopolysaccharide (LPS), activate the NF-B signaling pathway. NF-B is a family of dimeric transcription factors composed of members of the Rel family of DNA binding proteins, including NF-B1 (p50 and its precursor p105), NF-B2 (p52 and its precursor p100), c-Rel, RelA (p65), and RelB (11, 18). Upon stimulation, NF-B translocates into the nucleus, where it binds to specific DNA sequences and regulates transcription. NF-B is involved in mediating a wide spectrum of cellular responses, including infections, inflammation, and apoptosis (2, 27). Inappropriate regulation of NF-B is involved in a wide range of human diseases, including cancer, neurodegenerative disorders, arthritis, asthma, and chronic inflammation (3,4,10,12). The NF-B signaling pathway is tightly modulated at various levels by distinct regulatory proteins. For example, the binding of the IB family of proteins prevents the nuclear translocation of NF-B (16). However, a protein factor that can regulate the DNA binding activity of NF-B has not been documented.The PIAS (protein inhibitor of activated STAT) family of proteins consists of four members: PIAS1, PIAS3, PIASx, and PIASy (33). Members of the PIAS family have been suggested to regulate STAT-mediated transcription. Upon cytokine stimulation, PIAS binds to STAT and inhibits STAT-mediated gene activation (1,8,21,22). Among the PIAS family, PIAS1 and PIASy have been shown to inhibit STAT1-dependent transcription through distinct mechanisms. PIAS1 inhibits the transcriptional activity of STAT1 by blocking the DNA binding activity of STAT1. In contrast, PIASy does not affect the DNA binding activity of STAT1. It has been suggested that PIASy may act as a transcriptional corepressor of STAT1. The PIAS family of proteins has also been suggested to regulate a number of other transcription factors, including nuclear hormone receptors (13,28,36,37), LEF1 (31), and p53 (15,26,32).To understand the physiological role of PIAS1, we have recently generated Pias1 null mice (23). Detailed gene activation analysis indicates that PIAS1 selectively regulates a subset of interferon (IFN)-inducible genes. The antiviral activity of IFNs is significantly enhanced by Pias1 disruption. In addition, Pias1 null mice show enhanced protection against pathogenic infection. These results support a physiological role of PIAS1 in the negative regulation of IFN-activated STAT1-mediated gene activation and demonstrate an important role of PIAS1 in innate immune responses.Since STAT1 and the Rel family of proteins share structural similarity in their DNA binding domains (6), we explored the possible involvement of PIAS1 in the regulation of NF-B. Here we report that PIAS1 interacts with ...

Prolonged duration of closed suction drains is a strong independent risk factor for SSI following instrumented spinal fusion procedures. Therefore, removing drains as early as possible may lower infection rates.

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.