Behavioral Engagement and Activation Model Study (BEAMS): A latent class analysis of adopters and non-adopters of digital health technologies among people with Type 2 diabetes.

Many people with Type 2 diabetes (T2D) who could benefit from digital health technologies (DHTs) are either not using DHTs or do use them, but not for long enough to reach their behavioral or metabolic goals. We aimed to identify subgroups within DHT adopters and non-adopters and describe their unique profiles to better understand the type of tailored support needed to promote effective and sustained DHT use across a diverse T2D population. We conducted latent class analysis of a sample of adults with T2D who responded to an internet survey between December 2021 and March 2022. We describe the clinical and psychological characteristics of DHT adopters and non-adopters, and their attitudes toward DHTs. A total of 633 individuals were characterized as either DHT "Adopters" (n = 376 reporting any use of DHT) or "Non-Adopters" (n = 257 reporting never using any DHT). Within Adopters, three subgroups were identified: 21% (79/376) were "Self-managing Adopters," who reported high health activation and self-efficacy for diabetes management, 42% (158/376) were "Activated Adopters with dropout risk," and 37% (139/376) were "Non-Activated Adopters with dropout risk." The latter two subgroups reported barriers to using DHTs and lower rates of intended future use. Within Non-Adopters, two subgroups were identified: 31% (79/257) were "Activated Non-Adopters," and 69% (178/257) were "Non-Adopters with barriers," and were similarly distinguished by health activation and barriers to using DHTs. Beyond demographic characteristics, psychological, and clinical factors may help identify different subgroups of Adopters and Non-Adopters.

In this study, we characterized subgroups of adopters and non-adopters of digital health technologies (DHTs) for managing Type 2 diabetes, such as apps to track nutrition, continuous glucose monitors, and activity monitors like Fitbit. Self-efficacy for diabetes management, health activation, and perceived barriers to use DHT emerged as characteristics that distinguished subgroups. Notably, subgroups of adopters differed in their interest to use these technologies in the next 3 months; groups with low levels of self-efficacy and health activation were least interested in using them and thus at risk of discontinuing use. The ability to identify these subgroups can inform strategies tailored to each subgroup that motivate adoption of DHTs and promote long-term engagement.

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study.

BACKGROUND: In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost. OBJECTIVE: This study aimed to determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes. METHODS: Here, we present the development of PSYCHE-D (Prediction of Severity Change-Depression), a predictive model developed using PGHD from more than 4000 individuals, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable. RESULTS: PSYCHE-D is composed of 2 Light Gradient Boosting Machine (LightGBM) algorithm-based classifiers that use a range of PGHD input features, including objective activity and sleep, self-reported changes in lifestyle and medication, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an increase in depression severity over a 3-month interval, with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity while maintaining specificity when compared with a random model. CONCLUSIONS: These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual's mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals experiencing depression.

Development and qualification of a machine learning algorithm for automated hair counting.

OBJECTIVE: Determining the amount of hair on the scalp has always been an important metric of patient satisfaction for hair growth and hair retention technologies. While simple in concept, this measurement is a difficult, resource intensive task for the dermatologist and the research scientist. Specifically, counting and measuring hair in phototrichogram images is very time consuming and labour intensive. Due to cost, often only a fraction of available images is manually analysed. There is a need for an automated method that can significantly increase speed and throughput while reducing the cost of counting and measuring hair in phototrichogram images. METHODS: Recent advances in machine learning and deep convolutional neural networks (deep learning) have led to a revolution in the analysis of image, video, speech, text and other sensor data. Image diagnostics have seen remarkable improvements with completely automated methods outperforming both human experts and human-engineered analysis methods. Deep learning methods can also provide speed and cost benefits. To enable use of a deep learning, we created a data set of 288 manually annotated phototrichogram images with marked location and length of each hair (the training dataset). We designed a custom neural network architecture and custom image processing algorithms to best utilize the available training data and to maximize performance for hair counting and length measurement. The performance of the algorithm was qualified by comparing hair count and length measurements to an independent ground truth method, the semi-manual Canfield's Hair Metrix method. RESULTS: Leveraging deep neural networks, we have developed capability to apply machine learning to reduce the time needed to acquire data from phototrichograms of patients' scalp from months to seconds. Our algorithm enables fast and fully automated hair counting and length measurement. The algorithm shows high agreement with human manually assisted analysis (ground truth). CONCLUSIONS: We have trained and deployed an algorithm utilizing this technology and have demonstrated the reproducibility, accuracy and speed of this algorithm that, once deployed, requires little to no recurring cost or manual intervention for its operation. The method allows fast analysis of large number of images, reducing study cost and significantly reducing study analysis time.

Age-induced and photoinduced changes in gene expression profiles in facial skin of Caucasian females across 6 decades of age.

BACKGROUND: Intrinsic and extrinsic factors, including ultraviolet irradiation, lead to visible signs of skin aging. OBJECTIVE: We evaluated molecular changes occurring in photoexposed and photoprotected skin of white women 20 to 74 years of age, some of whom appeared substantially younger than their chronologic age. METHODS: Histologic and transcriptomics profiling were conducted on skin biopsy samples of photoexposed (face and dorsal forearm) or photoprotected (buttocks) body sites from 158 women. 23andMe genotyping determined genetic ancestry. RESULTS: Gene expression and ontologic analysis revealed progressive changes from the 20s to the 70s in pathways related to oxidative stress, energy metabolism, senescence, and epidermal barrier; these changes were accelerated in the 60s and 70s. The gene expression patterns from the subset of women who were younger-appearing were similar to those in women who were actually younger. LIMITATIONS: Broader application of these findings (eg, across races and Fitzpatrick skin types) will require further studies. CONCLUSIONS: This study demonstrates a wide range of molecular processes in skin affected by aging, providing relevant targets for improving the condition of aging skin at different life stages and defining a molecular pattern of epidermal gene expression in women who appear younger than their chronologic age.

Tissue-engineered constructs of human oral mucosa examined by Raman spectroscopy.

A noninvasive quality monitoring of tissue-engineered constructs is a required component of any successful tissue-engineering technique. During a 2-week production period, ex vivo produced oral mucosa-equivalent constructs (EVPOMEs) may encounter adverse culturing conditions that might compromise their quality and render them ineffective. We demonstrate the application of near-infrared Raman spectroscopy to in vitro monitoring of EVPOMEs during their manufacturing process, with the ultimate goal of applying this technology in situ to monitor the grafted EVPOMEs. We identify Raman spectroscopic failure indicators for less-than optimal EVPOMEs that are stressed by higher temperature and exposure to higher than normal concentration of calcium ions. Raman spectra of EVPOMEs exposed to thermal and calcium stress showed correlation of the band height ratio of CH(2) deformation to phenylalanine ring breathing modes, providing a Raman metric to distinguish between viable and nonviable constructs. We compared these results to histology and glucose consumption measurements, demonstrating that Raman spectroscopy is more sensitive and specific to changes in proteins' secondary structure not visible by H&E histology. We also exposed the EVPOMEs to rapamycin, a cell growth inhibitor and cell proliferation capacity preserver, and distinguished between EVPOMEs pretreated with 2 nM rapamycin and controls, using the ratio of the Amide III envelope to the phenylalanine band as an indicator.