Physical activity is associated with changes in knee cartilage microstructure

Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device

PURPOSE

Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive.

METHODS

We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors.

RESULTS

Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%.

CONCLUSION

These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.

Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective

Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.

Acute effects of a single bout of exercise therapy on knee acoustic emissions in patients with osteoarthritis: a double-blinded, randomized controlled crossover trial

BACKGROUND

Knee osteoarthritis is associated with higher kinetic friction in the knee joint, hence increased acoustic emissions during motion. Decreases in compressive load and improvements in movement quality might reduce this friction and, thus, sound amplitude. We investigated if an exercise treatment acutely affects knee joint sounds during different activities of daily life.

METHODS

Eighteen participants with knee osteoarthritis (aged 51.8 ± 7.3 years; 14 females) were included in this randomized crossover trial. A neuromuscular exercise intervention and a placebo laser needle acupuncture treatment were performed. Before and after both interventions, knee joint sounds were measured during three different activities of daily living (standing up/sitting down, walking, descending stairs) by means of vibroarthrography. The mean amplitude (dB) and the median power frequency (MPF, Hz) were assessed at the medial tibial plateau and the patella. Differences in knee acoustic emissions between placebo and exercise interventions were calculated by analyses of covariance.

RESULTS

Controlled for participant's age, knee demanding activity level and osteoarthritis stage, the conditions significantly differed in their impact on the MPF (mean(± SD) pre-post-differences standing up: placebo: 9.55(± 29.15) Hz/ exercise: 13.01(± 56.06) Hz, F = 4.9, p < 0.05) and the amplitude (standing up: placebo:0.75(± 1.43) dB/ exercise: 0.51(± 4.68) dB, F = 5.0, p < 0.05; sitting down: placebo: 0.07(± 1.21) dB/ exercise: -0.16(± .36) dB, F = 4.7, p < 0.05) at the tibia. There were no differences in the MPF and amplitude during walking and descending stairs (p > 0.05). At the patella, we found significant differences in the MPF during walking (placebo 0.08(± 1.42) Hz/ exercise: 15.76(± 64.25) Hz, F = 4.8, p < .05) and in the amplitude during descending stairs (placebo: 0.02 (± 2.72) dB/ exercise: -0.73(± 2.84) dB, F = 4.9, p < 0.05). There were no differences in standing up/ sitting down for both parameters, nor in descending stairs for the MPF and walking for the amplitude (p > 0.05).

CONCLUSION

The MPF pre-post differences of the exercise intervention were higher compared to the MPF pre-post differences of the placebo treatment. The amplitude pre-post differences were lower in the exercise intervention. In particular, the sound amplitude might be an indicator for therapy effects in persons with knee osteoarthritis.

TRIAL REGISTRATION

The study was retrospectively registered in the German Clinical Trials Register ( DRKS00022936 , date of registry: 26/08/2020).

Mindset is associated with future physical activity and management strategies in individuals with knee osteoarthritis

BACKGROUND

Despite the benefits of physical activity for individuals with knee osteoarthritis (KOA), physical activity levels are low in this population.

OBJECTIVES

We conducted a repeated cross-sectional study to compare mindset about physical activity among individuals with and without KOA and to investigate whether mindset relates to physical activity.

METHODS

Participants with (n = 150) and without (n = 152) KOA completed an online survey at enrollment (T1). Participants with KOA repeated the survey 3 weeks later (T2; n = 62). The mindset questionnaire, scored from 1 to 4, assessed the extent to which individuals associate the process of exercising with less appeal-focused qualities (e.g., boring, painful, isolating, and depriving) versus appeal-focused (e.g., fun, pleasurable, social, and indulgent). Using linear regression, we examined the relationship between mindset and having KOA, and, in the subgroup of KOA participants, the relationship between mindset at T1 and self-reported physical activity at T2. We also compared mindset between people who use medication for management and those who use exercise.

RESULTS

Within the KOA group, a more appeal-focused mindset was associated with higher future physical activity (β=38.72, p = 0.006) when controlling for demographics, health, and KOA symptoms. Individuals who used exercise with or without pain medication or injections had a more appeal-focused mindset than those who used medication or injections without exercise (p<0.001). A less appeal-focused mindset regarding physical activity was not significantly associated with KOA (β = -0.14, p = 0.067). Further, the mindset score demonstrated strong internal consistency (α = 0.92; T1; n = 150 and α = 0.92; T2; n = 62) and test-retest reliability (intraclass correlation coefficient (ICC) > 0.84, p < 0.001) within the KOA sample.

CONCLUSIONS

In individuals with KOA, mindset is associated with future physical activity levels and relates to the individual's management strategy. Mindset is a reliable and malleable construct and may be a valuable target for increasing physical activity and improving adherence to rehabilitation strategies involving exercise among individuals with KOA.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

OBJECTIVE

We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.

DESIGN

We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison.

RESULTS

Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values.

CONCLUSIONS

Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

Linking physical activity with clinical, functional, and structural outcomes: an evidence map using the Osteoarthritis Initiative

Physical activity is consistently recommended across clinical practice guidelines for managing knee osteoarthritis, yet prescription rates are low. Evidence mapping uses a systematic approach to visually illustrate and summarize published evidence, highlight gaps in the literature, and formulate research questions. The purpose of this study was to review and summarize evidence published from the Osteoarthritis Initiative (OAI) linking physical activity with clinical, functional, and structural knee osteoarthritis outcomes. Electronic databases were searched until June 2021. Studies from the OAI reporting subjective (Physical Activity Scale for the Elderly, PASE) or objective (accelerometry) physical activity data were included. Scatter plots were created to represent each outcome group (clinical, functional, structural) and physical activity measure (PASE, accelerometry) to map the evidence by the directional effect (positive, interaction, negative, or no effect) associated with physical activity. Forty-two articles were included in this review. Physical activity was quantified using PASE (n = 21), accelerometry (n = 20), or both (n = 1). Studies reported consistently positive physical activity effects on clinical (n = 22) and functional (n = 20) outcomes, with few exceptions. Structural (n = 15) outcomes were largely reported as interaction effects by physical activity intensity or sex, or as no significant effect. A network of interconnected outcomes emerged, with clinical and functional outcomes often reported together, and structural outcomes reported individually. This study provides an overview of current evidence linking physical activity to multiple interrelated knee osteoarthritis outcomes using an OAI-driven model. These evidence maps can be used as a framework to guide future investigations of the effects of physical activity on knee osteoarthritis.

Validity of and recommendations for knee joint acoustic assessments during different movement conditions

Knee joint sounds contain information on joint health, morphology and loading. These acoustic signals may be elicited by further, as yet unknown factors. By assessing potential elicitors and their relative contributions to the acoustic signal, we investigated the validity of vibroarthrographic assessments during different movement conditions with the aim to derive recommendations for their practical usage. Cross-sectional study. Nineteen healthy participants (24.7 ± 2.8 yrs, 7 females) performed five movements: level walking, descending stairs, standing up, sitting down, and forward lunge. Knee joint sounds were recorded by two microphones (medial tibial plateau, patella). Knee joint kinematics and ground reaction forces were recorded synchronously to calculate knee joint moments (Nm/Kg). The mean amplitude (dB) and the median power frequency (Hz) were determined. A repeated measures mixed model investigated the impact of potential predictors (sagittal, frontal, transverse plane and total knee joint moments, knee angular velocity, age, sex, body mass index (BMI) and Tegner Activity Score (TAS)). Most of the amplitudes variance is explained by between-subject differences (tibia: 66.6%; patella: 75.8%), and of the median power frequencies variance by the movement condition (tibia: 97.6%; patella: 98.9%). The final model revealed several predictor variables for both sensors (tibia: sagittal plane, frontal plane, and total knee joint moments, age, and TAS; patella: sagittal plane knee moments, knee angular velocity, TAS). The standardization of the execution of the activities, a between-group matching of variables and the inclusion of co-variates are recommended to increase the validity of vibroarthrographic measurements during different movement conditions of the knee joint.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Osteoarthritis year in review 2019: imaging

OBJECTIVE

To provide a narrative review of original articles on osteoarthritis (OA) imaging published between April 1, 2018 and March 30, 2019.

METHODS

All original research articles on OA imaging published in English between April 1, 2018 and March 30, 2019 were identified using a PubMed database search. The search terms of "Osteoarthritis" or "OA" were combined with the search terms "Radiography", "X-Rays", "Magnetic Resonance Imaging", "MRI", "Ultrasound", "US", "Computed Tomography", "Dual Energy X-Ray Absorptiometry", "DXA", "DEXA", "CT", "Nuclear Medicine", "Scintigraphy", "Single-Photon Emission Computed Tomography", "SPECT", "Positron Emission Tomography", "PET", "PET-CT", or "PET-MRI". Articles were reviewed to determine relevance based upon the following criteria: 1) study involved human subjects with OA or risk factors for OA and 2) study involved imaging to evaluate OA disease status or OA treatment response. Relevant articles were ranked according to scientific merit, with the best publications selected for inclusion in the narrative report.

RESULTS

The PubMed search revealed a total of 1257 articles, of which 256 (20.4%) were considered relevant to OA imaging. Two-hundred twenty-six (87.1%) articles involved the knee joint, while 195 (76.2%) articles involved the use of magnetic resonance imaging (MRI). The proportion of published studies involving the use of MRI was higher than previous years. An increasing number of articles were also published on imaging of subjects with joint injury and on deep learning application in OA imaging.

CONCLUSION

MRI and other imaging modalities continue to play an important role in research studies designed to better understand the pathogenesis, progression, and treatment of OA.

pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation

Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.

Results

We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.

Availability and implementation

Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.

Supplementary information

Supplementary data are available at Bioinformatics online.