Analysis Of Longitudinal Data Research Articles

Revised Recommendations on Methods for Assessing Multimorbidity Changes over Time: Aligning the Method to the Purpose.

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. We identify and discuss methods aligned to specific research objectives with the goals of 1) establishing a common language for assessing longitudinal changes in multimorbidity, 2) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and 3) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with four general typologies for research objectives in the longitudinal study of multimorbidity: 1) examine individual change in multimorbidity, 2) identify sub-groups that follow similar trajectories of multimorbidity progression, 3) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and 4) examine the co-progression of multimorbidity with key health domains. This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

The well-being equation: How inner fulfilment drives the impact of older persons on the Ghanaian society.

Understanding and managing the complex processes of ageing is a critical function of gerontological nursing, especially when it comes to older people's well-being and their contributions to society. Globally, older persons contribute in many ways to families and communities. However, the relationship between older person's overall well-being and their propensity to contribute to society remains an important gap in research. The study examined the association between well-being and the impact of older persons on Ghanaian society. A secondary analysis of longitudinal survey data of the 2014/15 Study on Global Ageing and Adult Health (SAGE Wave 2) conducted by the World Health Organization was used. The multilevel logistic regression technique was used to examine four dimensions of well-being and their associations with high social contribution among older persons. The output was reported as odds ratios (OR). The results show that older persons who had high physical and psychological well-being were more likely to contribute to society (OR = 1.25, 95% CI = 0.93, 1.68), (OR = 1.75, 95% CI = 1.32, 2.33). However, those with high levels of emotional and spiritual well-being were less likely to make social contributions (OR = 0.66, 95% CI = 0.49, 0.88), (OR = 0.88, 95% CI = 0.66, 1.18). This study shows a positive association between well-being and older persons' societal impact. Good mental and physical health encourage societal involvement among older persons, while high emotional and spiritual well-being may lead to less societal contribution. These findings are important for nursing policies promoting social contribution and well-being among older persons 60 years and over in Ghana.

Disease Course of Clinically Isolated Optic Neuritis.

Optic neuritis is the most common optic neuropathy in young adults and a frequent manifestation of multiple sclerosis. Its clinical course is pertinent to the design of visual pathway neuroprotection trials. This is a secondary analysis of longitudinal data from the TONE trial, which included 103 patients from 12 German academic tertiary centers with acute unilateral optic neuritis as a clinically isolated syndrome and baseline high-contrast visual acuity <0.5 decimal. Patients were randomized to 1,000 mg methylprednisolone i.v./d plus either erythropoietin (33,000 IU/d) or placebo (saline solution) for 3 days. They were followed up at standardized intervals with a battery of tests including high-contrast visual acuity, low-contrast letter acuity, contrast sensitivity, visual fields, visual evoked potentials, and retinal optical coherence tomography. At 6 months, participants answered a standardized questionnaire on vision-related quality of life (NEI-VFQ 25). We describe the disease course with mixed-effects piecewise linear models and calculate structure-function correlations using Pearson r. Because erythropoietin had no effect on the visual system, we use pooled (treatment-agnostic) data. Patients experienced initial rapid and then decelerating improvements of visual function with thinning of inner and thickening of outer retinal layers. At 6 months, visual parameters were positively correlated with inner and negatively correlated with outer retinal thickness changes. Peripapillary retinal nerve fiber layer thinning predominantly occurred in sectors without previous swelling. At 6 months, macular ganglion cell and inner plexiform layer thinning was weakly correlated with the P100 peak time (r = -0.11) and moderately correlated with the amplitude of visual evoked potentials (r = 0.35). Only functional outcomes were at least moderately correlated with vision-related quality of life. The longitudinal data from this large study cohort may serve as a reference for the clinical course of acute optic neuritis. The pattern of correlation between visual evoked potentials and inner retinal thinning may argue that the latter is mostly due to ganglion cell loss, rather than dysfunction. Visual pathway neuroprotection trials with functional outcomes are needed to confirm that candidate drugs will benefit patients' vision-related quality of life. ClinicalTrials.gov, NCT01962571.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using an Explainable AI Approach

Mild Cognitive Impairment (MCI) is a cognitive state frequently observed in older adults, characterized by significant alterations in memory, thinking, and reasoning abilities that extend beyond typical cognitive decline. It is worth noting that around 10–15% of individuals with MCI are projected to develop Alzheimer’s disease, effectively positioning MCI as an early stage of Alzheimer’s. In this study, a novel approach is presented involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer’s disease during the MCI stage. The methodology entails utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through the analysis of longitudinal data, spanning from the baseline visit to the 12-month follow-up, a predictive model was constructed. The proposed model calculates, over a 36-month period, the likelihood of progression from MCI to Alzheimer’s disease, achieving an accuracy rate of 85%. To further enhance the precision of the model, this study implements feature selection using the Recursive Feature Elimination technique. Additionally, the Shapley method is employed to provide insights into the model’s decision-making process, thereby augmenting the transparency and interpretability of the predictions.

Differential sex-related effect of left ventricular ejection fraction trajectory on the risk of mortality and heart failure readmission following hospitalization for acute heart failure: A longitudinal study.

There is limited information on the sex-specific longitudinal changes of left ventricular ejection fraction (LVEF) after an acute heart failure (AHF) hospitalization. We aimed to investigate whether LVEF trajectories over time and their impact on mortality and AHF readmission rates differ between men and women. We conducted a retrospective sex-specific analysis of longitudinal LVEF measurements (n = 9581) in 3383 patients with an index hospitalization for AHF in a single tertiary-level hospital. Statistical techniques suited for longitudinal data analysis were used. The mean age of the sample was 73.8 ± 11.2 years, and 47.9% were women. The mean LVEF was 49.4 ± 15.3%. At a median follow-up of 2.58 years (interquartile range 0.77-5.62), we registered 2197 deaths (64.9%) and 2597 AHF readmissions in 1302 (38.5%) patients. The longitudinal analysis showed that women had consistently higher LVEF values throughout the follow-up with both trajectories characterized by an early peak-approximately at 1 year-followed by decreasing values in men but a plateau in women. Multivariate between-sex comparisons across LVEF categories revealed that women had lower rates of AHF readmissions when LVEF ≤40%. On the contrary, women displayed an excess risk of AHF readmissions when LVEF >60%. A trend in the same direction was found for cardiovascular and all-cause mortality. Sex was a significant factor in determining the follow-up trajectory of LVEF and predicting differences in outcomes after an AHF admission. The findings suggest that women have a higher risk of AHF readmissions at higher LVEF values, while men have a higher risk at lower LVEF values. For all-cause and cardiovascular mortality, the same direction of the association was inferred but they were not significant.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

Trends and determinants of mental illness in humanitarian migrants resettled in Australia: Analysis of longitudinal data.

Given the frequent exposure of humanitarian migrants to traumatic or stressful circumstances, there exists a potential predisposition to mental illness. Our objective was to pinpoint the trends and determinants of mental illness among humanitarian migrants resettled in Australia. This study considered five waves of longitudinal data involving humanitarian migrants resettled in Australia. Post-traumatic stress disorder (PTSD) and psychological distress were assessed using PTSD-8 and Kessler-6 screening tools. Through a Generalised Linear Mixed model (GLMM), variables displaying a 95% CI that excluded the value of 1.0 for the odds ratio were identified as associated factors for both PTSD and elevated psychological distress. The selection of multivariable covariates was guided by causal loop diagrams and least absolute shrinkage and selection operators methods. At baseline, there were 2399 humanitarian migrants with 1881 retained and at the fifth yearly wave; the response rate was 78.4%. PTSD prevalence decreased from 33.3% (95% CI: 31.4-35.3) at baseline to 28.3% (95% CI: 26.2-30.5) at year 5. Elevated psychological distress persisted across all waves: 17.1% (95% CI: 15.5-18.6) at baseline and 17.1% (95% CI: 15.3-18.9) at year 5. Across the five waves, 34.0% of humanitarian migrants met screening criteria for mental illness, either PTSD or elevated psychological distress. In the multivariate model, factors associated with PTSD were loneliness (AOR 1.5, 95% CI: 1.3-1.8), discrimination (AOR 1.6: 1.2-2.1), temporary housing contract (AOR 3.7: 2.1-6.7), financial hardship (AOR 2.2:1.4-3.6) and chronic health conditions (AOR 1.3: 1.1-1.5), whereas the associated factors for elevated psychological distress were loneliness (AOR 1.8: 1.5-2.2), discrimination (AOR 1.7: 1.3-2.2) and short-term lease housing (AOR 1.6: 1.0-1.7). The prevalence, persistence and consequential burden of mental illness within this demographic underscore the urgent need for targeted social and healthcare policies. These policies should aim to mitigate modifiable risk factors, thereby alleviating the significant impact of mental health challenges on this population.

Psychological and physical factors related to social integration of older adults in Ghana

BackgroundGlobally, older adults aged 60 years and over are outnumbering children under 5 and young people aged 15–24. Much evidence exists on the importance of high social integration and positive quality of life and health outcomes. However, evidence on how older adults are socially integrated in Ghanaian communities is limited. This study examined how self-reported well-being and quality of life (psychological and physical (psycho-physical) factors) predict the social integration of older adults in Ghana.MethodsA secondary analysis of longitudinal survey data of the 2014/15 Study on Global Ageing and Adult Health (SAGE Wave 2) conducted by the World Health Organization was applied. Older adults aged 60 years and older (n = 1,927) were included in this study. Multilevel logistic regression analyses were used to examine psycho-physical factors associated with high social integration among older adults. The output was reported as odds ratios (OR).ResultsIn general, social integration varied based on older adults’ demographic characteristics. Those in rural communities had lower odds of having high social integration (OR = 0.76, 95% CI = 0.56,1.03) when compared with older adults in urban areas. Having high physical and psychological well-being was associated with high social integration (OR = 1.90, 95% CI = 1.41, 2.57), (OR = 2.05, 95% CI = 1.56, 2.69). However, older adults with high levels of emotional and spiritual well-being were 9% and 7% (respectively) less likely to experience a high level of social integration (OR = 0.94, 95% CI = 0.71,1.24), (OR = 0.79, 95% CI = 0.60,1.04).ConclusionThe higher the level of self-reported psychological and physical well-being, the higher the social integration for older adults aged 60 years and over. However, the higher the level of self-reported emotional well-being and spiritual well-being, the less likely to have high social integration. Improved social integration or participation in society for older adults with high emotional and spiritual well-being is needed. The findings of this study highlight the need for policymakers and stakeholders to consider psycho-physical factors as an important public health tool and metric to encourage more research on the well-being of older adults in Ghana.

A review of common methods used in the analysis of human microbiome sequencing data

The past two decades have seen a rapid rise in the development and use of sequencing technologies. The advent of high-throughput sequencing (HTS) has allowed scientists sequence the genomic content of entire microbial communities rapidly and affordably. Methods for data management, visualization, and analysis of this microbiome sequencing data are now widespread in open and closed source software tools. In this review, we identify common analysis methods used in microbiome sequencing studies, including methods for normalisation of abundance table data, visualization of high-dimensional microbiome data and the analysis of longitudinal microbiome sequencing data. In particular we find that a very small proportion of metagenomic studies made use of compositional data analysis methods and none that made use of compositional methods for longitudinal microbiome data.

Neuropsychiatric symptoms and white matter hyperintensities in older adults without dementia.

We aimed to examine associations between neuropsychiatric symptoms (NPS) and white matter hyperintensities (WMH) status in older adults without dementia under the hypothesis that WMH increased the odds of having NPS. Longitudinal analysis of data acquired from the National Alzheimer's Coordinating Center Uniform Data Set. Data were derived from 46 National Institute on Aging - funded Alzheimer's Disease Research Centers. NACC participants aged ≥50 years with available data on WMH severity with a diagnosis of mild cognitive impairment (MCI) or who were cognitively unimpaired (CU) were studied. Among 4617 CU participants, 376 had moderate and 54 extensive WMH. Among 3170 participants with MCI, 471 had moderate and 88 had extensive WMH. Using Cardiovascular Health Study (CHS) scores, WMH were coded as no to mild (CHS score: 0-4), moderate (score: 5-6) or extensive (score: 7-8). NPS were quantified on the Neuropsychiatric Inventory Questionnaire. Binary logistic regression models estimated the odds of reporting each of 12 NPS by WMH status separately for individuals with MCI or who were CU. Compared to CU individuals with no to mild WMH, the odds of having elation [9.87, (2.63-37.10)], disinhibition [4.42, (1.28-15.32)], agitation [3.51, (1.29-9.54)] or anxiety [2.74, (1.28-5.88)] were higher for the extensive WMH group, whereas the odds of having disinhibition were higher for the moderate WMH group [1.94, (1.05-3.61)]. In the MCI group, he odds of NPS did not vary by WMH status. Extensive WMH were associated with higher odds of NPS in CU older adults but not in those with MCI.

Head Impact Exposure in Hawaiian High School Football: Influence of Adherence Rates on a Helmetless Tackling and Blocking Training Intervention.

High school football remains a popular, physically demanding sport despite the known risks for acute brain and neck injury. Impacts to the head also raise concerns about their cumulative effects and long-term health consequences. To examine the effectiveness of a helmetless tackling training program to reduce head impact exposure in football participants. A three-year, quasi-experimental, prospective cohort (clinicaltrials.gov #NCTXXX) study. Honolulu (XXX, XXX) area public and private secondary schools with varsity and junior varsity football. Football participants (n=496) ages 14 to 18 years old. Intervention(s) Participants wore new football helmets furnished with head impact sensor technology. Teams employed a season-long helmetless tackling and blocking intervention in Years 2 and 3 consisting of a 3-phase, systematic progression of 10 instructional drills. Head impact frequency per athlete exposure (ImpAE), location, and impact magnitude per participant intervention adherence levels (60% and 80%). An overall regression analysis revealed a significant negative association between ImpAE and adherence (p=0.003, beta=-1.21, SE=0.41). In year 3, a longitudinal data analysis of weekly ImpAE data resulted in an overall difference between the adherent and non-adherent groups (p=0.040 at 80%; p=0.004 at 60%), mainly due to decreases in top and side impacts. Mean cumulative impact burden for the adherent group (n=131: 2,105.84g ± 219.76,) was significantly (p=0.020) less than the non-adherent group (n=90: 3,158.25g ± 434.80) at the 60% adherence level. Participants adhering to the intervention on at least a 60% level experienced a 34% to 37% significant reduction in the number of head impacts (per exposure) through the season. These results provide additional evidence that a helmetless tackling and blocking training intervention (utilizing the HuTT® program) reduces head impact exposure in high school football players. Adherence to an intervention is crucial for achieving intended outcomes.

Does the Use of Media and Other Information Sources Cause the Perceived Experience of Climate Change or is it the Other Way Around?

ABSTRACT The perceived experience of climate change can be an important motivator of individual climate action. Recent scholarship argued that the use of media and other information sources is a basis of perceived experience. The current study analyzed a cross-lagged panel model to know if the use of information causes perceived experience or vice versa. Analysis of longitudinal data from a sample of Singapore residents (N = 466) showed that the use of traditional media, but not social media or interpersonal sources, explained change in perceived experience. In contrast, perceived experience explained change in the use of traditional media and interpersonal communication. We discuss these findings vis-à-vis the reinforcing spirals model of media effects and offer practical insights for climate change communicators, especially journalists.

Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation.

The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes, which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, dynamic treatment regime estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the weighted proportional odds model: a regression based, approximate doubly-robust approach to single-stage dynamic treatment regime estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of weighted proportional odds model with our adjusted weights via simulation studies. We further extend weighted proportional odds model to multi-stage dynamic treatment regime estimation with household interference, namely dynamic weighted proportional odds model. Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.

Negative religious coping versus spiritual struggles: Moderator or main effect?

While previous research indicates that negative religious coping relates with distress, it is unclear if this represents generalized spiritual struggles or a specific vulnerability activated by negative events. Moreover, past research treats coping as a stable phenomenon, although it likely fluctuates. This research simultaneously tested both models longitudinally and examined the relationship between coping and distress. The current study is a secondary analysis of longitudinal data collected online over 3 years from 397 participants. Life events, negative religious coping, and depressive symptoms were assessed on six occasions, and linear mixed-effect models were used to analyze data. Negative religious coping was largely stable with increases at times of stress. Participants with higher mean negative coping reported more depressive symptoms regardless of life circumstances (i.e., main effect), likely reflecting generalized spiritual struggles. In addition, those with higher mean coping or with particularly high levels at a given time reported even higher levels of symptoms when they experienced more negative life events (i.e., moderation effect). Results suggest that previous research merges two distinct phenomena-spiritual struggles and negative coping. This has important theoretical and research implications and suggests that integrating spirituality and religion into treatment can be warranted.

Multivariate mixed-effects ordinal logistic regression models with difference-in-differences estimator of the impact of WORTH Yetu on household hunger and socioeconomic status among OVC caregivers in Tanzania.

Although most of the livelihood programmes target women, those that involve women and men have been evaluated as though men and women were a single homogenous population, with a mere inclusion of gender as an explanatory variable. This study evaluated the impact of WORTH Yetu (an economic empowerment intervention to improve livelihood outcomes) on household hunger, and household socioeconomic status (SES) among caregivers (both women and men) of orphaned and vulnerable children (OVC) in Tanzania. The study hypothesized that women and men respond to livelihood interventions differently, hence a need for gender-disaggregated impact evaluation of such interventions. This is a secondary analysis of longitudinal data, involving caregivers' baseline (2016-2019) and follow-up (2019-2020) data from the USAID Kizazi Kipya project in 25 regions of Tanzania. Two dependent variables (ie, outcomes) were assessed; household hunger which was measured using the Household Hunger Scale (HHS), and Socioeconomic Status (SES) using the Principal Component Analysis (PCA). WORTH Yetu, a livelihood intervention implemented by the USAID Kizazi Kipya project was the main independent variable whose impact on the two outcomes was evaluated using multivariate analysis with a multilevel mixed-effects, ordinal logistic regression model with difference-in-differences (DiD) estimator for impact estimation. The analysis was based on 497,293 observations from 249,655 caregivers of OVC at baseline, and 247,638 of them at the follow-up survey. In both surveys, 70% were women and 30% were men. Their mean age was 49.3 (±14.5) years at baseline and 52.7 (±14.8) years at the follow-up survey. Caregivers' membership in WORTH Yetu was 10.1% at the follow-up. After adjusting for important confounders there was a significant decline in the severity of household hunger by 46.4% among WORTH Yetu members at the follow-up compared to the situation at the baseline (adjusted Odds Ratio (aOR) = 0.536, 95% Confidence Interval (CI) [0.521, 0.553]). The decline was 45.7% among women (aOR = 0.543 [0.524, 0.563]) and 47.5% among men (aOR = 0.525 [0.497, 0.556]). Regarding SES, WORTH Yetu members were 15.9% more likely to be in higher wealth quintiles at the follow-up compared to the situation at the baseline (aOR = 1.159 [1.128, 1.190]). This impact was 20.8% among women (aOR = 1.208 [1.170, 1.247]) and 4.6% among men (aOR = 1.046 [0.995, 1.101]). WORTH Yetu was associated with a significant reduction in household hunger, and a significant increase in household SES among OVC caregivers in Tanzania within an average follow-up period of 1.6 years. The estimated impacts differed significantly by gender, suggesting that women and men responded to the WORTH Yetu intervention differently. This implied that the design, delivery, and evaluation of such programmes should happen in a gender responsive manner, recognising that women and men are not the same with respect to the programmes.

De Casteljau's algorithm in geometric data analysis: Theory and application

For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing and discrete geometry, to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependent, geometric data. This article provides a survey of the recent theoretical developments in this exciting area as well as its applications in fields such as geometric morphometrics and longitudinal data analysis in medicine, archaeology, and meteorology.

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.

During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population. This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial. A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups. Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21). These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

Daily spiritual experiences and allostatic load trajectories: a longitudinal study of midlife African American women.

This study aimed to evaluate the association between daily spiritual experiences and allostatic load (AL) trajectories in midlife African American women. A longitudinal analysis of public-use data from 727 African American women in the Study of Women's Health Across the Nation (SWAN) was performed. We included African American women who completed the Daily Spiritual Experiences Scale at SWAN visit 4 (2000-2001) and had AL data at three or more study visits over 7 years. AL was calculated at each visit using 10 biomarkers: systolic and diastolic blood pressure, body mass index, C-reactive protein, high-density lipoprotein cholesterol, total cholesterol, waist-to-hip ratio, fasting serum glucose, triglycerides, and dehydroepiandrosterone. Group-based trajectory modeling identified women with similar patterns of AL. We used multinomial logistic regression to estimate associations between daily spiritual experiences (some days or less, most days, daily, many times a day) and AL trajectories. Our sample had a mean ± SD age of 49.9 ± 2.66 years, 47% were early perimenopausal, and 17% earned <$19,999 annually. The mean ± SD AL score was 2.52 ± 1.68. Three AL trajectories were identified: low (35.1%), moderate (44.7%), and high (20.2%). In age-adjusted models, women who reported daily comfort in religion and spirituality were less likely to follow a high AL trajectory (odds ratio, 0.41; 95% CI, 0.18-0.93); the association was attenuated when controlling for depressive symptoms (odds ratio, 0.48; 95% CI, 0.19-1.21). Findings from this study do not support an independent association between spirituality in African American women and AL trajectories in midlife. Studies with a larger sample and additional measures of spirituality are warranted in this population.