A Life Course of Its Own: Trajectories and Correlates of Misconduct Among Those With Long-Term Sentences

The obesity epidemic is a global concern. Standard behavioral treatment including increased physical activity, reduced energy intake, and behavioral change counseling is an effective lifestyle intervention for weight loss. To identify distinct step count patterns among weight loss intervention participants, examine weight loss differences by trajectory group, and examine baseline factors associated with trajectory group membership. Both groups received group-based standard behavioral treatment while the experimental group received up to 30 additional, one-on-one self-efficacy enhancement sessions. Data were analyzed using group-based trajectory modeling, analysis of variance, chi-square tests, and multinomial logistic regression. Participants (N = 120) were mostly female (81.8%) and white (73.6%) with a mean (SD) body mass index of 33.2 (3.8)kg/m2. Four step count trajectory groups were identified: active (>10,000steps/day; 11.7%), somewhat active (7500-10,000 steps/day; 28.3%), low active (5000-7500steps/day; 27.5%), and sedentary (<5000steps/day; 32.5%). Percent weight loss at 12 months increased incrementally by trajectory group (5.1% [5.7%], 7.8% [6.9%], 8.0% [7.4%], and 13.63% [7.0%], respectively; P = .001). At baseline, lower body mass index and higher perceived health predicted membership in the better performing trajectory groups. Within a larger group of adults in a weight loss intervention, 4 distinct trajectory groups were identified and group membership was associated with differential weight loss.

The aim of the study was to assess patterns of longitudinal changes in caries status among school-going children in Singapore. Dental records for a single cohort of students who received dental examinations in six standard examination years between 2009 and 2017 were analysed (n = 24 699). Group-based trajectory modelling with a zero-inflated Poisson distribution was carried out to determine dental caries trajectories in the permanent dentition. Associations between sociodemographic factors and trajectory group membership were assessed using multinomial logistic regression. The predicted population distribution across the four caries trajectory groups identified was 65.0% ('none'), 16.8% ('low'), 14.8% ('medium') and 3.4% ('high'). The 'none' trajectory group had a decayed, missing and filled teeth (DMFT) score of 0 throughout the 8 years. Higher baseline DMFT counts and nonlinear increases in DMFT scores were noted for the 'low', 'medium' and 'high' trajectory groups. The correlation coefficient between DMFT counts in years 6 and 8 was 0.91, as compared to 0.77 between baseline and year 1. Factors associated with the 'high' caries trajectory include lower socio-economic status, female gender, Chinese race (compared to the Indian race), enrolment in primary schools in the Eastern and Western regions of Singapore, and enrolment in public secondary schools. Under a nationwide school dental service, four trajectory patterns of caries counts in the permanent dentition were identified over 8 years. Among students in the 'low', 'medium' and 'high' trajectory groups, greater caries increment was noted during the transition from primary to secondary school. The correlation between DMFT counts in successive examinations was stronger in older than younger ages.

PMS51 TRAJECTORIES OF FOLLOW-UP COMPLIANCE AND THEIR PREDICTORS IN A FRACTURE LIAISON SERVICE

Survivorship from critical illness has improved; however, factors mediating the functional recovery of persons experiencing a critical illness remain incompletely understood. To identify groups of acute respiratory failure (ARF) survivors with similar patterns of physical function recovery after discharge and to determine the characteristics associated with group membership in each physical function trajectory group. We performed a secondary analysis of a randomized controlled trial, using group-based trajectory modeling to identify distinct subgroups of patients with similar physical function recovery patterns after ARF. Chi-square tests and one-way analysis of variance were used to determine which variables were associated with trajectory membership. A multinomial logistic regression analysis was performed to identify variables jointly associated with trajectory group membership. A total of 260 patients enrolled in a trial evaluating standardized rehabilitation therapy in patients with ARF and discharged alive (NCT00976833) were included in this analysis. Physical function was quantified using the Short Physical Performance Battery at hospital discharge and 2, 4, and 6 months after enrollment. Latent class analysis of the Short Physical Performance Battery scores identified four trajectory groups. These groups differ in both the degree and rate of physical function recovery. A multinomial logistic regression analysis was performed using covariates that have been previously identified in the literature as influencing recovery after critical illness. By multinomial logistic regression, age (P < 0.001), female sex (P = 0.001), intensive care unit (ICU) length of stay (LOS) (P = 0.003), and continuous intravenous sedation days (P = 0.004) were the variables that jointly influenced trajectory group membership. Participants in the trajectory demonstrating most rapid and complete functional recovery consisted of younger females with fewer continuous sedation days and a shorter LOS. The participant trajectory that failed to functionally recover consisted of older patients with greater sedation time and the longest LOS. We identified distinct trajectories of physical function recovery after critical illness. Age, sex, continuous sedation time, and ICU length of stay impact the trajectory of functional recovery after critical illness. Further examination of these groups may assist in clinical trial design to tailor interventions to specific subgroups.

BackgroundAlthough quality of life (QOL) improves over time for most breast cancer patients after their treatment, some patients may show different patterns of QOL. Beyond determining distinct QOL trajectories, identifying characteristics of patients who have different trajectories can help identify breast cancer patients who may benefit from intervention. We aimed to identify trajectories of QOL in breast cancer patients for one year after the end of primary treatment, to determine the factors influencing these changes.MethodsThis longitudinal study recruited 140 breast cancer patients. Patients' QOL, symptom experience, self-efficacy, and social support were assessed using the Functional Assessment of Cancer Therapy Scale-G, Memorial Symptom Assessment Scale-Short Form, Self-Efficacy Scale for Self-Management of Breast Cancer, and Interpersonal Support Evaluation List-12. Data were collected immediately after the end of primary treatment (T1) and at three (T2), six (T3), and 12 months (T4) after primary treatment. Group-based trajectory modeling was used to identify distinct subgroups of patients with similar patterns of QOL change after treatment. A one-way analysis of variance was used to determine which variables were associated with trajectory membership. A multinomial logistic regression was performed to identify factors associated with trajectory group membership.ResultsWe analyzed 124 patients (mean age: 48.75 years). Latent class analysis of the QOL identified three trajectory groups: the low QOL group (n = 27; 21.1%), moderate QOL group (n = 57; 45.3%), and high QOL group (n = 40; 33.6%). The low QOL group showed consistently low QOL after the end of primary treatment, and the moderate QOL group showed a slight decrease in QOL from T1 to T3, which returned to the T1 level at T4. The high QOL group maintained a consistently high QOL. By multinomial logistic regression, psychological symptoms (odds ratio [OR] 0.46, 95% confidence interval [CI] 0.22–0.99) predicted a moderate QOL, and both psychological symptoms (OR 0.19, 95% CI 0.07–0.51) and belonging support (OR 1.60, 95% CI 1.06–2.39) predicted a high QOL.ConclusionIdentifying high-risk groups for reduced QOL after the end of primary treatment is necessary. Moreover, psychosocial interventions should be provided to alleviate psychological symptoms and increase belonging support to enhance patients' QOL.Trial registration Not registered.

Lifestyle modifications including physical activity and dietary changes are considered the cornerstone approach for behavioral obesity management. However, there is a lack of knowledge about long-term trajectory patterns during interventions. This study aims to (1) assess physical activity and dietary fat intake trajectory during a 24-month intensive lifestyle intervention; and (2) identify baseline characteristics and sociodemographic predictors associated with different behavioral trajectory patterns. The PROPEL trial recruited and randomly assigned primary care clinics to a 2-year intensive lifestyle intervention or a usual care group. A total of 439 individuals (88.4% female, 73.8% Black) enrolled in the intervention group. Participants were included in the analysis if they provided valid data for physical activity and dietary fat intake at a minimum of 2 of the 4 assessment time points. Physical activity was measured via the short-form International Physical Activity Questionnaire, and the percentage of energy from fat was measured using a validated fat screener. A group-based trajectory model was used to identify the physical activity and dietary fat intake trajectory groups. Multilevel multinomial logistic regression models were used with the dependent variable of trajectory group membership, adjusting for baseline covariates. All statistical analyses were performed using R statistical software (version 4.4.2, macOS), and P < .05 was considered statistically significant. Trajectory analysis identified 5 groups of physical activity and dietary fat intake over 24 months. Group 1 (21%) maintained moderate physical activity with a gradual decrease after 6 months, accompanied by an initial fat reduction that plateaued at 31%. Group 2 (16%) had low activity levels while showing a U-shaped fat intake during the intervention. Group 3 (33%) reached a peak of high activity at 6 months before declining, maintaining the lowest percentage of fat intake. Group 4 (20%) increased activity through 12 months with a reverse U-shaped change and with fat reduction, while group 5 (10%) also had low activity and persistently high fat intake. Multinomial regression indicated that participants with diabetes at baseline were more likely to be classified as group 5 with a lower activity level and high dietary fat intake. Identifying the trajectory of behavior can facilitate the development of more tailored interventions, particularly for long-term behavior change.

Introduction: Type 1 diabetes is a chronic condition that affects adolescents’ quality of life and raises the risk of developing mental health concerns and diabetes-related complications. Measuring glycated hemoglobin (HbA1c) over time is the standard of care within the management of type 1 diabetes; however, the determinants of different HbA1c trajectories remain poorly understood. In the secondary analysis of the data collected for the Integrated Care Model (1), we aimed to identify groups of HbA1c trajectories with similar trends and examine the association between these groups and demographic and psychosocial variables. Methods: HbA1c data were collected at 4 consecutive time points with a gap of 3±1 months. We used Leffondré’s method (2) and Group-based trajectory modeling (GBTM) (3) to derive the groups of HbA1c trajectories among 91 adolescents. Baseline characteristics of the adolescents included in the groups were analyzed by univariate analysis. Results: Leffondré’s method identified three groups of trajectories: stable (63%), decreasing (17%), and increasing (20%). The baseline HbA1c levels for the three groups were 8.00±0.93, 10.07±1.63, and 8.21±1.14, respectively. Among the baseline characteristics, only the treatment method distinguished the groups of adolescents with similar trajectories of HbA1c over time (p=0.015). The GBTM method identified similar groups: stable (67%), decreasing (18%) and increasing (15%). The baseline HbA1c levels for the three groups were 7.89±0.88, 9.81±1.61, and 9.00±1.38, respectively. Groups produced by GBTM were also distinguished by treatment modality at baseline (p=0.022). Discussion: We identified three distinct patterns of HbA1c trajectories in adolescents. The only baseline characteristic that significantly distinguished these trajectories was the treatment modality.

Loneliness trajectories and the effects of adverse childhood experience among middle-aged and older Chinese adults.

BackgroundResearch on health-related quality of life (HRQoL) trajectory patterns for people with disabilities (PwD) is scant. Understanding the HRQoL trajectory patterns for PwDs and investigating their relationship with disability types and socioeconomic factors can have important implications for Australia’s welfare policy.MethodsWe analysed data from waves 11 to 21 of the Household, Income and Labour Dynamics in Australia (HILDA) survey of respondents aged 15 + years of the PwDs. The analytic sample consists of 3724 self-reported disabled individuals and 34,539 observations in total. The SF-6D utility score is our HRQoL measure. Group-based trajectory modelling was utilised to identify trajectory groups, and multinomial logistic regression was employed to determine the baseline factors associated with trajectory group membership.ResultsThe study identified four distinct types of HRQoL trajectories (high, moderate improving, moderate deteriorating and low HRQoL trajectories). Psychosocial disability types followed by physical disability types had a high Relative Risk Ratio (RRR) in the low group compared with high trajectory group membership of PwDs (psychosocial: 6.090, physical: 3.524). Similar, results followed for the moderate improving group albeit with lower RRR (psychosocial: 2.868, Physical: 1.820). In the moderate deteriorating group, the disability types were not significant as this group has a similar profile to high group at the baseline. Compared with males, females had a higher RRR in low and moderate versus high improving HRQoL trajectories (low: 1.532, moderate improving: 1.237). Comparing the richest class to the poorest class, socioeconomic factors (income and education) predicted significantly lower exposure for the richer class to the low and medium HRQoL trajectories groups (RRR < 1).ConclusionDifferent forms of disability, demographic and socioeconomic factors have distinct effects on the HRQoL trajectories of disabled individuals. Healthcare and economic resource efficiency might be improved with targeted government policy interventions based on disability trajectories.

PurposeThis study aimed to (1) identify the trajectories of prescribed antidepressants in refugee youth and matched Swedish-born peers diagnosed with common mental disorder (CMD) and (2) characterize the trajectories according to sociodemographic and medical factors.MethodsThe study population comprised 2,198 refugees and 12,199 Swedish-born individuals with both Swedish-born parents, aged 16–25 years in 2011, residing in Sweden and treated in specialised healthcare for CMD 2009–11. Group-based trajectory modelling was used to identify different trajectory groups of antidepressant use-based on annual defined daily dosages (DDDs). Multinomial logistic regression was applied to investigate the association of sociodemographic and medical characteristics with the identified trajectories. Nagelkerke pseudo-R2 values were estimated to evaluate the strength of these associations.ResultsFour trajectory groups of antidepressant use among young refugees were identified with following proportions and DDD levels in 2011: ‘low constant’ (88%, < 100), ‘low increasing’ (2%, ≈710), ‘medium decreasing’ (8%, ≈170) and ‘high increasing’ (2%, ≈860). Similar trajectories, however, with different proportions were identified in Swedish-born: 67%, 7%, 21% and 5%, respectively. The most influential factors discriminating the trajectory groups among refugees were ‘duration of stay in Sweden’ (R2 = 0.013), comorbid ‘other mental disorders’ (R2 = 0.009) and ‘disability pension’ (R2 = 0.007), while ‘disability pension’ (R2 = 0.017), comorbid ‘other mental disorders’ (R2 = 0.008) and ‘educational level’ (R2 = 0.008) were the most important determinants discriminating trajectory groups among Swedish-born youth.ConclusionThe lower use of antidepressants in refugees with CMDs compared to their Swedish-born counterparts warrants health literacy programs for refugees and training in transcultural psychiatry for healthcare professionals.

To identify distinct diet trajectories after breast cancer (BC) diagnosis, and to examine the characteristics associated with diet trajectories. We analyzed 2865 Pathways Study participants who completed ≥ 2 food frequency questionnaires at the time of BC diagnosis (baseline), and at 6 and 24months after baseline. Trajectory groups of fruit and vegetable (F/V) intake, % calories from dietary fat, and alcohol intake over 24months were identified using group-based trajectory modeling. Associations between diet trajectories and sociodemographic, psychosocial, and clinical factors were analyzed using multinomial logistic regression. Analyses identified 3 F/V trajectory groups, 4 dietary fat groups, and 3 alcohol groups. All 3 F/V trajectory groups reported slightly increased F/V intake post-diagnosis (mean increase = 0.2-0.5 serving/day), while 2 groups (48% of participants) persistently consumed < 4 servings/day of F/V. Dietary fat intake did not change post-diagnosis, with 45% of survivors maintaining a high-fat diet (> 40% of calories from fat). While most survivors consumed < 1 drink/day of alcohol at all times, 21% of survivors had 1.4-3.0 drinks/day at baseline and temporarily decreased to 0.1-0.5 drinks/day at 6months. In multivariable analysis, diet trajectory groups were significantly associated with education (ORs: 1.93-2.49), income (ORs: 1.32-2.57), optimism (ORs: 1.93-2.49), social support (OR = 1.82), and changes in physical well-being (ORs: 0.58-0.61) and neuropathy symptoms after diagnosis (ORs: 1.29-1.66). Pathways Study participants reported slightly increasing F/V and decreasing alcohol intake after BC diagnosis. Nearly half of survivors consumed insufficient F/V and excessive dietary fat. It is important to prioritize nutrition counseling and education in BC survivors.

Background Post-stroke cognitive impairment (PSCI) is one of the major complications of stroke and exhibits a dynamic progression. This study uses the Group-Based Trajectory Model (GBTM) to fit cognitive development trajectories in stroke patients and analyze the influencing factors and trends of PSCI across trajectory groups. It aims to facilitate early identification and intervention for high-risk PSCI patients, providing a theoretical basis for optimizing intervention strategies. Methods The study employs a longitudinal design and completed the follow-up of 729 patients across five hospitals. The MoCA scale is used to assess the cognitive function of stroke patients at baseline, 1 month, 3 months, and 6 months. GBTM is employed to fit the cognitive development trajectories of PSCI, and multinomial logistic regression is used to analyze its influencing factors. Results The GBTM results show that there are four post-stroke cognitive development trajectories in stroke patients, namely, severe PSCI group, mild PSCI group, PSCI risk group and normal cognitive group. Advanced age, high NIHSS score, living alone, fatigue, malnutrition, and risk of malnutrition are risk factors for PSCI. High ADL scores, higher education levels, living in urban areas, urban workers medical insurance, and high average monthly family income are protective factors for PSCI (P<0.05). Conclusions Post-stroke cognitive development trajectories exhibit heterogeneity. In clinical practice, it is recommended that healthcare professionals pay attention to early screening, early diagnosis, early intervention, and early treatment of PSCI, to improve or delay the development of PSCI by preventing or treating the primary disease.

A three-armed drinking cessation trial in Vietnam found that both a brief and intensive version of an intervention effectively reduced hazardous drinking in people living with HIV. We used group-based trajectory modeling (GBTM) to assess the extent to which findings may vary by latent subgroups distinguished by their unique responses to the intervention. Using data on drinking patterns collected over the 12months, GBTM identified five trajectory groups, three of which were suboptimal ["non-response" (17.2%); "non-sustained response" (15.7%), "slow response" (13.1%)] and two optimal ["abstinent" (36.4%); "fast response" (17.6%)]. Multinomial logistic regression was used to determine that those randomized to any intervention arm were less likely to be in a suboptimal trajectory group, even more so if randomized to the brief (vs. intensive) intervention. Older age and higher baseline coping skills protected against membership in suboptimal trajectory groups; higher scores for readiness to quit drinking were predictive of it. GBTM revealed substantial heterogeneity in participants' response to a cessation intervention and may help identify subgroups who may benefit from more specialized services within the context of the larger intervention.

Prescription opioids are essential in managing pain among adults with chronic pain conditions. However, persistent use over time can lead to negative health consequences. Identifying individuals with persistent use over time and their characteristics can inform clinical decision-making and aid in reducing the risk of abuse and overdose deaths. This study aims to examine trajectories of prescription opioid use over time and factors associated with these trajectories among older cancer survivors with any non-cancer pain conditions (NCPC). We conducted a retrospective cohort study design with longitudinal data of older (age at cancer diagnosis ≥67years) cancer (incident breast, colorectal, and prostate cancers, or non-Hodgkin lymphoma) survivors with any NCPC. Data were derived from the 2007-2015 linked Surveillance, Epidemiology, and End Results (SEER)-Medicare dataset (N = 35,071). Group-Based Trajectory Modeling (GBTM) was used to identify homogeneous subgroups (distinct trajectories) of individuals based on every 90-day prescription opioid use during pre-cancer diagnosis (t1-t4), acute cancer treatment (t5-t8), and post-cancer treatment (t9-t12) periods. Biological factors, social determinants of health (SDoH), physical and mental health, medication use, health care use, and external factors associated with a trajectory membership were analyzed with multivariable multinomial logistic regressions. Four distinct trajectories of opioid use were identified: (1) increase-decrease use (6.1%); (2) short-term use after cancer diagnosis (40.6%); (3) low-use (41.0%); and (4) persistent use (12.3%). In the fully-adjusted multinomial logistic regression, the SDoH such as Non-Hispanic Black [adjusted odds ratios (AOR) = 1.69; 95%CI = 1.48, 1.93)] and rural residence (AOR = 1.49; 95%CI = 1.15, 1.94)], comorbid anxiety (AOR = 1.33; 95%CI = 1.18, 1.51), and medication use (NSAIDs - AOR = 1.20; 95%CI = 1.10, 1.30) were associated with membership in the persistent use group. Persistent use was less likely among those with higher fragmented care index (AOR = 0.95, 95%CI = 0.93, 0.97) and those living in counties with higher Medicare advantage penetration (AOR = 0.96; 95%CI = 0.95, 0.97). One in eight older adults had persistent opioid use over time. The profile characteristics of this group were different from the other trajectory groups. Policies and programs to reduce chronic opioid use need to consider the intra- and inter-individual variability to reduce opioid-related morbidity and mortality.

Labour market marginalisation (LMM), i.e. severe problems in finding and keeping a job, is common among young adults with attention-deficit/hyperactivity disorder (ADHD). This study aimed to disentangle the extent of LMM as well as the heterogeneity in patterns of LMM among young adults with ADHD and what characterises those belonging to these distinct trajectories of LMM. This population-based register study investigated all 6287 young adults, aged 22-29 years, who had their first primary or secondary diagnosis of ADHD in Sweden between 2006 and 2011. Group-based trajectory (GBT) models were used to estimate trajectories of LMM, conceptualised as both unemployment and work disability, 3 years before and 5 years after the year of an incident diagnosis of ADHD. Odds ratios (ORs) with 95% confidence intervals (CIs) for the association between individual characteristics and the trajectory groups of LMM were estimated by multinomial logistic regression. Six distinct trajectories of LMM were found: 'increasing high' (21% belonged to this trajectory group) with high levels of LMM throughout the study period, 'rapidly increasing' (19%), 'moderately increasing' (21%), 'constant low' (12%) with low levels of LMM throughout the study period, 'moderately decreasing' (14%) and finally 'fluctuating' (13%), following a reversed u-shaped curve. Individuals with the following characteristics had an increased probability of belonging to trajectory groups of increasing LMM: low educational level (moderately increasing: OR: 1.4; CI: 1.2-1.8, rapidly increasing: OR: 1.7; CI: 1.3-2.1, increasing high: OR: 2.9; CI: 2.3-3.6), single parents (moderately increasing: OR: 1.6; CI: 1.1-2.4, rapidly increasing: OR: 2.0; CI: 1.3-3.0), those born outside the European Union/the Nordic countries (rapidly increasing: OR: 1.7; CI: 1.1-2.5, increasing high: OR: 2.1; CI: 1.4-3.1), persons living in small cities/villages (moderately increasing: OR: 2.4; CI: 1.9-3.0, rapidly increasing: OR: 2.1; CI: 1.6-2.7, increasing high: OR: 2.6; CI: 2.0-3.3) and those with comorbid mental disorders, most pronounced regarding schizophrenia/psychoses (rapidly increasing: OR: 6.7; CI: 2.9-19.5, increasing high: OR: 12.8; CI: 5.5-37.0), autism spectrum disorders (rapidly increasing: OR: 4.6; CI: 3.1-7.1, increasing high: OR: 9.6; CI: 6.5-14.6), anxiety/stress-related disorders (moderately increasing: OR: 1.3; CI: 1.1-1.7, rapidly increasing: OR: 2.0; CI: 1.6-2.5, increasing high: OR: 1.8; CI: 1.5-2.3) and depression/bipolar disorder (moderately increasing: OR: 1.3; CI: 1.0-1.6, rapidly increasing: OR: 1.7; CI: 1.4-2.2, increasing high: OR: 1.5; CI: 1.2-1.9). About 61% of young adults were characterised by increasing LMM after a diagnosis of ADHD. To avoid marginalisation, attention should especially be given to young adults diagnosed with ADHD with a low educational level, that are single parents and who are living outside big cities. Also, young adults with comorbid mental disorders should be monitored for LMM early in working life.