Health Behavior Data Research Articles

Comparison of Predictive Models for Prevention of Missed Endoscopy Appointments- failure of a Predictive Model to Outperform Overbooking Model.

Patient late cancelation and nonattendance for endoscopy appointments is an ongoing problem affecting the productivity and wait times of endoscopy units. Previous research evaluated a model for predictive overbooking and had promising results. All endoscopy visits at an outpatient endoscopy unit during 4 nonconsecutive months were included in the data analysis. Patients who did not attend their appointment, or canceled with 48 hours of their appointment were considered nonattendees. Demographic, health, and prior visit behavior data was collected and the groups compared. 1780 patients attended 2331 visits in the study period. Comparing the attendee versus non-attendees, there were significant differences in mean age, prior absenteeism, prior cancelations, and total number of hospital visits. No significant differences were seen between groups in winter versus non-winter months, the day of the week, sex distribution, type of procedure booked, or whether the referral was from specialist clinic or direct to procedure. The visit cancelation proportion (calculated excluding current visit) was substantially higher in the absentee group (P<0.0001). A predictive model was developed and compared to current booking as well as a straight overbooking of 7%. Both overbooking models performed better than the current practice, but the predictive overbooking model did not outperform straight overbooking. Developing an endoscopy unit specific predictive model may not be more beneficial than straight overbooking as calculated by missed appointment percentage.

Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students.

Since the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states. We used data from a large, multi-site cross-sectional survey consisting of 17 universities in Southeast Asia. This research work models mental well-being and reports on the performance of various machine learning algorithms, including generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. Random Forest and adaptive boosting algorithms achieved the highest accuracy for identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include the number of sports activities per week, body mass index, grade point average (GPA), sedentary hours, and age. Based on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.

Income inequality and daily use of cannabis, cigarettes, and e-cigarettes among Canadian secondary school students: Results from COMPASS 2018-19.

Cannabis, cigarette, and e-cigarette use among Canadian adolescents is a major public health concern. Income inequality has been associated with adverse mental health among youth and may contribute to the risk of frequent cannabis, cigarette, and e-cigarette use. We tested the association between income inequality and the risk of daily cannabis, cigarette, and e-cigarette use among Canadian secondary school students. We used individual-level survey data from Year 6 (2018/19) of Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary Behavior (COMPASS) and area-level data from the 2016 Canadian Census. Three-level logistic models were used to assess the relationship between income inequality and adolescent daily and current cannabis use, cigarette smoking, and e-cigarette use. The analytic sample included 74,501 students aged 12-19. Students were most likely to report being male (50.4%), white (69.1%), and having weekly spending money over $100 (23.5%). We found that a standard deviation unit increase in Gini coefficient was significantly associated with increased likelihood of daily cannabis use (OR=1.25, 95% CI=1.01-1.54) when adjusting for relevant covariates. We found no significant relationship between income inequality and daily smoking. While Gini was not significantly associated with daily e-cigarette use, we observed a significant interaction between Gini and gender (OR=0.87, 95% CI= 0.80-0.94), indicating that increased income inequality was associated with higher risk of reporting daily e-cigarette use among females only. An association between income inequality and the likelihood of reporting daily cannabis use across all students and daily e-cigarette use in females were observed. Schools in higher income inequalityareasmay benefit from targeted prevention and harm reduction programs. Resultsemphasize the need for upstream discussion on policies that can mitigate thepotentialeffects income inequality.

Psychosocial and behavioral outcomes in the adult workforce during the COVID-19 pandemic: a 1-Year longitudinal survey

BackgroundEfforts to limit the spread of COVID-19 have included public space closures, mask usage, and quarantining. Studies regarding the impact of these measures on the psychosocial and behavioral health outcomes of the workforce have focused frequently on healthcare employees. To expand the literature base, we deployed a one-year longitudinal survey among mostly non-healthcare employees assessing changes in select psychosocial outcomes, health behaviors, and COVID-19-related transmission prevention behaviors and perceptions.MethodsWe deployed the CAPTURE baseline survey across eight companies from November 20, 2020-February 8, 2021. The baseline survey included questions on psychosocial outcomes, health behaviors, and COVID-19 transmission prevention behaviors, with several questions containing a retrospective component to cover the time period prior to the pandemic. Additional questions on vaccination status and social support were subsequently added, and the updated survey deployed to the same baseline participants at three, six, and 12 months after baseline survey deployment. We analyzed data descriptively and performed Friedman’s and subsequent Wilcoxon-signed rank tests, as appropriate, to compare data within and between time points.ResultsA total of 3607, 1788, 1545, and 1687 employees completed the baseline, 3-month, 6-month, and 12-month CAPTURE surveys, respectively, with 816 employees completing all four time points. Employees reported higher stress, anxiety, fatigue, and feelings of being unsafe across all time points compared to pre-pandemic. Time spent sleeping increased initially but returned to pre-pandemic levels at follow-up. Lower rates of physical activity and higher rates of non-work screen time and alcohol consumption relative to pre-pandemic were also reported. Over 90% of employees perceived wearing a mask, physical distancing, and receiving the COVID-19 vaccine as ‘moderately’ or ‘very important’ in preventing the spread of COVID-19 across all time points.ConclusionsRelative to pre-pandemic, poorer psychosocial outcomes and worsened health behaviors were observed across all time points, with values worse at the baseline and 12-month time points when COVID-19 surges were highest. While COVID-19 prevention behaviors were consistently deemed to be important by employees, the psychosocial outcome and health behavior data suggest the potential for harmful long-term effects of the pandemic on the well-being of non-healthcare employees.

Automated wearable cameras for improving recall of diet and time use in Uganda: a cross-sectional feasibility study

BackgroundTraditional recall approaches of data collection for assessing dietary intake and time use are prone to recall bias. Studies in high- and middle-income countries show that automated wearable cameras are a promising method for collecting objective health behavior data and may improve study participants’ recall of foods consumed and daily activities performed. This study aimed to evaluate the feasibility of using automated wearable cameras in rural Eastern Ugandan to collect dietary and time use data.MethodsMothers of young children (n = 211) wore an automated wearable camera on 2 non-consecutive days while continuing their usual activities. The day after wearing the camera, participants’ dietary diversity and time use was assessed using an image-assisted recall. Their experiences of the method were assessed via a questionnaire.ResultsMost study participants reported their experiences with the automated wearable camera and image-assisted recall to be good (36%) or very good (56%) and would participate in a similar study in the future (97%). None of the eight study withdrawals could be definitively attributed to the camera. Fifteen percent of data was lost due to device malfunction, and twelve percent of the images were "uncodable" due to insufficient lighting. Processing and analyzing the images were labor-intensive, time-consuming, and prone to human error. Half (53%) of participants had difficulty interpreting the images captured by the camera.ConclusionsUsing an automated wearable camera in rural Eastern Uganda was feasible, although improvements are needed to overcome the challenges common to rural, low-income country contexts and reduce the burdens posed on both participants and researchers. To improve the quality of data obtained, future automated wearable camera-based image assisted recall studies should use a structured data format to reduce image coding time; electronically code the data in the field, as an output of the image review process, to eliminate ex post facto data entry; and, ideally, use computer-assisted personal interviews software to ensure completion and reduce errors. In-depth formative work in partnership with key local stakeholders (e.g., researchers from low-income countries, representatives from government and/or other institutional review boards, and community representatives and local leaders) is also needed to identify practical approaches to ensuring that the ethical rights of automated wearable camera study participants in low-income countries are adequately protected.

Cohort profile: Biomarkers related to folate-dependent one-carbon metabolism in colorectal cancer recurrence and survival – the FOCUS Consortium

PurposeThe overarching goal of the FOCUS (biomarkers related to folate-dependent one-carbon metabolism in colorectal cancer (CRC) recurrence and survival) Consortium is to unravel the effect of folate and folate-mediated one-carbon...

Mental health and lifestyle behaviors of students in a doctor of chiropractic program.

Health professional students' mental health and lifestyle behaviors may impact their personal health as well as their clinical practice. The objective of our retrospective study was to see if there were changes in students' mental health and lifestyle behaviors during their doctor of chiropractic program (DCP). In 2019, we identified health center files for 198 students who graduated from our DCP between 2015 and 2016, of which 69% (n = 137) contained Health Insurance Portability and Accountability Act (HIPAA) consent forms granting permission for file review. From each of these files, we extracted mental health and lifestyle behavior data that students self-reported at the beginning and end of their DCP. Data were analyzed with descriptive statistics, paired t test, sign test, and McNemar's chi-square test. The majority of our students reported drinking alcohol, engaging in regular exercise, and not smoking at the beginning and end of our DCP. Comparing pre and post data, there was a statistically significant increase in water, fruit, and vegetable consumption along with an increased proportion of students reporting eating junk food and drinking 1 or more servings of caffeine (p ≤ .002). The proportion of students reporting anxiety or depression decreased between the beginning and end of our DCP (p = .002). This study provided information about student mental health and lifestyle behaviors at 1 DCP. We recommend future studies use validated questionnaires across multiple DCPs. There also may be a need for intervention research to decrease DCP student alcohol use.

Associations between a weighted health behaviour score, socioeconomic status, and all-cause mortality in UK Biobank cohort: a prospective analysis

BackgroundUnhealthy behaviours are associated with disproportionate mortality among socioeconomically deprived populations. Previous studies exploring that disproportionate harm do not examine weighted scores, or examine few behaviours. We aimed to create an extended weighted health behaviour score and examine the effect of socioeconomic status on the association between score and all-cause mortality. MethodsData was sourced from the UK Biobank population cohort, recruited in 2006–10. The main exposures included in the analysis were 11 health behaviours (baseline self-report): smoking status, alcohol consumption, physical activity, time spent watching television, sleep duration, added salt in diet, social isolation, intake of red meat, intake of processed meat, intake of oily fish, and intake of fruit and vegetables. Behaviours were classified as healthy or unhealthy according to national guidelines or latest evidence. Socioeconomic deprivation was measured with the Townsend deprivation index. Cox proportional hazard models of health behaviour data were prospectively linked to death registries to examine associations between health behaviours and all-cause mortality. Models were adjusted for demographics and health at baseline. Mortality associated with each behaviour alone was used to determine score weighting. For sensitivity analysis, we explored associations between weighted lifestyle score and all-cause mortality stratified by sex and ethnicity. FindingsThe analysis included 229 107 participants with complete data. Median age was 53 years (IQR 47–60) for 119 634 (52·2%) women and 54 years (47–60) for 109 473 (47·8%) men. Over a median follow-up of 11·9 years (IQR 11.1–12.6), 9379 (4·1%) participants died. Compared with having no unhealthy behaviours, each behaviour was positively associated with all-cause mortality. Smoking (hazard ratio [HR] 2·47 [95% CI 2·25–2·70]) and social isolation (1·69 [1·54–1·86]) were associated with notably higher mortality. A weighted score was created by ascribing one point to each 40% increment in risk (four points for smoking, two points for social isolation, and one points for each of all other behaviours). A dose-response increment for all-cause mortality HR was noted with each additional point of weighted score. Associations were stronger in more deprived tertiles. With least deprived and lowest score as reference, HRs for highest scores were 2·22 (95% CI 1·72–2·86) in the least deprived and 4·10 (3·62–4·65) in the most deprived. An additive interaction between sex and lifestyle score for all-cause mortality was suggested by the data; men had slightly higher HRs at each level of the lifestyle score. However, a statistical test for interaction on a multiplicative scale was not significant. No evidence was found of interaction (either additive or multiplicative) between ethnicity and lifestyle score. InterpretationAn extended weighted health behaviour score has strong associations with mortality, and associations are stronger in more deprived participants. Weighted health behaviour scores that account for socioeconomic deprivation could convey personalised risk and inform healthy living policy. Further work with adequate numbers of participants from minority ethnic groups is required to make more accurate estimates of mortality associated with a weighted health behaviour score in these populations. FundingHMEF is supported by a Medical Research Council Clinical Research Training Fellowship (grant number MR/T001585/1), which covered the costs of accessing the data herein.

Pre-injury health status of truck drivers with a workers’ compensation claim

Truck drivers are a vulnerable population due to the high number of workplace injuries and fatalities predominant in their occupation. In Australia, the road freight transportation industry has been identified as a national priority area in terms of creating preventative measures to improve the health and safety of its workers. With an environment conducive to poor nutritional food choices and unhealthy lifestyle behaviours, many barriers exist to creating a safe and healthy workforce. Thus, the current study aimed to describe the pre-injury hospital-recorded health conditions and health service use of truck drivers with a worker’s injury compensation claim/s when compared to workers in other industries. Data was obtained from a compensation claims database and linked with hospital admissions data recorded five years prior to the injury claim. Health and lifestyle behaviour data for the occupational code of truck drivers was compared to other occupational drivers, as well as to all other occupations. Analysis was conducted via logistic regression. The results found that when compared to other occupational drivers, truck drivers were significantly more likely to have a hospital-recorded diagnosis of diabetes and/or hypertension, as well as being significantly more likely to have a hospital record of tobacco use and/or alcohol misuse/abuse. The findings show that there is a need to review and revise existing health strategies to promote the health and wellbeing of truck drivers, especially given their challenging work environment.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2.

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Strategies and Lessons Learned During Cleaning of Data From Research Panel Participants: Cross-sectional Web-Based Health Behavior Survey Study

BackgroundThe use of web-based methods to collect population-based health behavior data has burgeoned over the past two decades. Researchers have used web-based platforms and research panels to study a myriad of topics. Data cleaning prior to statistical analysis of web-based survey data is an important step for data integrity. However, the data cleaning processes used by research teams are often not reported.ObjectiveThe objectives of this manuscript are to describe the use of a systematic approach to clean the data collected via a web-based platform from panelists and to share lessons learned with other research teams to promote high-quality data cleaning process improvements.MethodsData for this web-based survey study were collected from a research panel that is available for scientific and marketing research. Participants (N=4000) were panelists recruited either directly or through verified partners of the research panel, were aged 18 to 45 years, were living in the United States, had proficiency in the English language, and had access to the internet. Eligible participants completed a health behavior survey via Qualtrics. Informed by recommendations from the literature, our interdisciplinary research team developed and implemented a systematic and sequential plan to inform data cleaning processes. This included the following: (1) reviewing survey completion speed, (2) identifying consecutive responses, (3) identifying cases with contradictory responses, and (4) assessing the quality of open-ended responses. Implementation of these strategies is described in detail, and the Checklist for E-Survey Data Integrity is offered as a tool for other investigators.ResultsData cleaning procedures resulted in the removal of 1278 out of 4000 (31.95%) response records, which failed one or more data quality checks. First, approximately one-sixth of records (n=648, 16.20%) were removed because respondents completed the survey unrealistically quickly (ie, <10 minutes). Next, 7.30% (n=292) of records were removed because they contained evidence of consecutive responses. A total of 4.68% (n=187) of records were subsequently removed due to instances of conflicting responses. Finally, a total of 3.78% (n=151) of records were removed due to poor-quality open-ended responses. Thus, after these data cleaning steps, the final sample contained 2722 responses, representing 68.05% of the original sample.ConclusionsExamining data integrity and promoting transparency of data cleaning reporting is imperative for web-based survey research. Ensuring a high quality of data both prior to and following data collection is important. Our systematic approach helped eliminate records flagged as being of questionable quality. Data cleaning and management procedures should be reported more frequently, and systematic approaches should be adopted as standards of good practice in this type of research.

Health-risk behaviours among Indigenous Australians with diabetes: A study in the integrated Diabetes Education and Eye Screening (iDEES) project.

To assess the prevalence of modifiable health-risk behaviours among Indigenous Australian adults with diabetes attending a regional Victorian Indigenous primary-care clinic. A cross-sectional observational single-site study. As part of a multi-study project we administered the Smoking, Nutrition, Alcohol consumption, Physical activity and Emotional wellbeing (SNAPE) survey tool during the study baseline visit to methodically capture health-related behavioural data in the nurse-led integrated Diabetes Education and Eye Screening (iDEES) project in a regional Indigenous primary healthcare setting between January 2018 and March 2020. This descriptive SNAPE study helps address the lack of health behaviour data for Indigenous people with diabetes. Of 172 eligible adults, 135 (79%) were recruited to the iDEES study, 50 (37%) male. All participated in at least one survey. Median (range) age was 56 (46-67) years; 130 (96%) had Type 2 diabetes of median [IQR] duration 6 (2-12) years. All 135 provided smoking data; 88 (65%) completed all surveys. Forty-nine (36%) and 29 (22%) were current or former smokers, respectively; 5 (6%) met vegetable intake guidelines, 22 (25%) met fruit intake guidelines; 38 [43%] drank alcohol in the past year. On average, participants walked for ≥10min at a time 4days/week and sat for an average of 8h on weekdays; 35 (40%) had minimal-mild, and 30 (34%) had moderate-severe depressive symptoms. Suboptimal modifiable health-risk behaviours and depressive symptoms are common in Indigenous Australian adults with diabetes. Orderly assessment and reporting of health-risk behaviours using a single multi-component survey instrument (SNAPE tool) during a nurse-led diabetes education clinical visit is feasible and efficient. Such data may facilitate personalised interventions and improve diabetes management at both individual and health service levels.

A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data.

The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.

Abstract 61: Community Factors Associated With Persistently High 1-Year Recurrent Stroke Rates in the United States

Background: There is known geographic variation in recurrent stroke rates in the US; however, the contributions of socioeconomic status (SES), healthcare access/utilization, and community resources to these disparities are uncertain. We assessed community characteristics in counties having persistently higher recurrent stroke rates over a 16-year period. Methods: We included 3,485,618 fee-for-service Medicare beneficiaries aged ≥65y discharged with ischemic stroke from 2001-2016, grouped into four 4-year periods. We categorized 3221 US counties or equivalents into 6 groups based on the % of stroke patients with a recurrent stroke within 1 year. Persistently high-recurrence counties were those in the highest sextile for each 4-year period. We integrated county-specific demographic, geographic, SES, general health, care availability, health behavior, and environmental data from the US Census Bureau, USDA Economic Research Service, and Dartmouth Atlas. We calculated mean standardized differences in county characteristics between high-recurrence and other counties and used logistic regression to model high-recurrence counties as a function of 12 potentially modifiable county characteristics. Results: There were 133 persistently high-recurrence counties that were concentrated in the South Central US and included 140,144 stroke patients during the study (A; mean age 78.3y, 57% women, 82% White, 11.5% stroke recurrence vs 79.0 y, 55% women, 86% White, 10.5% stroke recurrence in other counties). Compared with the rest of the US, these counties had populations with lower SES, poorer health, more limited access to care providers and recreation/fitness, and reduced rates of preventive testing (B). The model including 12 potentially actionable characteristics had a c statistic of 0.84. Conclusions: Our findings highlight the value of identifying potentially modifiable community characteristics that, if improved, might reduce recurrent stroke rates.

Longitudinal Assessment of Relationships Between Health Behaviors and IL-6 in Overweight and Obese Pregnancy

Inflammation is a common factor in adverse pregnancy outcomes (APOs). Behavioral factors influence inflammatory markers and APOs but rarely have been investigated simultaneously in pregnancy. Our purpose was to determine how diet, physical activity, and obesity are associated with interleukin (IL)-6 in early and late pregnancy. We conducted a secondary analysis of 49 overweight/obese pregnant women. Health behavior data, including diet quality using the Dietary Inflammatory Index (DII®); physical activity (Leisure Time Physical Activity scale); body mass index (BMI); and plasma IL-6 concentrations were collected at 13-16 weeks (early pregnancy) and 34-36 weeks (late pregnancy) gestation. Multiple linear regression analyses were used to determine the amount of variance explained in early and late pregnancy IL-6 concentrations by early and late pregnancy diet, physical activity, and BMI. Early diet and early BMI were the strongest predictors of early IL-6 concentrations (R2 = 0.43; p < .001) and late IL-6 concentrations (R2 = 0.30; p < .001). Late BMI predicted late IL-6 (R2 = .11; p = .02). Change in diet over pregnancy predicted late IL-6 (R2 = 0.17; p = .03). These findings suggest that maternal diet and BMI in early pregnancy, which likely reflects prepregnancy status, may have a greater impact on inflammatory processes than factors later in pregnancy. Future work should assess if behavioral factors before pregnancy produce similar relationships to those reported here, which may clarify the timing and type of lifestyle interventions to effectively reduce APOs.

Prevalence and correlates of excess body weight in university students

abstract – This study aimed to identify the prevalence of excess body weight in university students from a representative state in Brazil's southern region and establish associations with the demographic, university environment, and health behavior correlates. A random sample of 5,310 university students answered an online questionnaire with demographic, university environment, and eight health behavior data. Excess body weight was identified using the body mass index (BMI ≥ 25 kg/m2). The data were processed using bivariate analysis and hierarchical multiple regression. The overall prevalence of excess body weight exposure was equivalent to 39.1% [35.7-42.6], with significantly different rates between gender, age, and marital status. Likewise, housing type and study year showed significant associations with excess body weight. Among health behavior indicators, with control of all other variables involved in the model, depressive symptoms (p = 0.031), high stress (p = 0.045), sleep duration &lt; 6 hours/night (p &lt; 0.001), moderate-vigorous physical activity &lt; 150 min/week (p = 0.022) and fruit/vegetable consumption &lt; 5 servings per day (p &lt; 0.001) were independently associated with the outcome. However, tobacco use, binge drinking, and prescription drug misuse did not remain in the adjusted multivariate model. In conclusion, the findings emphasize the importance of proposing and implementing multifaceted preventive intervention actions to prevent health outcomes related to excess body weight, once since significant associations with modifiable factors have been identified.

Weight outcomes in children with developmental disabilities from a multidisciplinary clinic

BackgroundChildren with developmental disabilities experience disparately high rates of obesity yet there are few reports detailing clinical outcomes for this population. AimTo describe outcomes of obesity treatment for children with developmental disabilities and a comparison group of children without developmental disabilities. Methods and proceduresWe examined weight outcomes of children with and without developmental disabilities seen in a family-centered, multidisciplinary treatment center over a ten-year period. We stratified by age and developmental disability diagnosis. We assessed whether intake demographic or health behavior data was associated with successful reduction of adiposity over six and twelve month follow-up periods, using a ≥5% absolute reduction in percent over the 95th percentile body mass index (BMIp95) as the primary outcome. Outcomes and resultsOver a ten-year period, 148 of 556 children in the obesity clinic (27 %) had a developmental disability. In children <12 years of age, 36 % of children with developmental disabilities reduced their adiposity compared with 18 % of children without developmental disabilities at six months, p = .01. This pattern continued at twelve months. Active transport to school was associated with reduced adiposity for those without a disability. Older children with disabilities rarely had a significant reduction (2 of 26 children), and they took more medications with weight-related side effects. Conclusions and implicationsYounger children with developmental disabilities experienced relative success in reducing their adiposity. Challenges to addressing obesity in this population include structural barriers to physical activity and medications for behavioral management with weight-related side effects.

Health behaviours of Indigenous and non-Indigenous cancer survivors living in regional and remote geographic areas of Australia: a short report

Abstract Background: Health behaviors are becoming increasingly recognized as important for cancer survivors in optimizing health after diagnosis. The current study aims to describe adherence to health behaviour guidelines in a sample of Indigenous and non-Indigenous cancer patients in Australia's Northern Territory. Methods: A structured survey collecting self-reported sociodemographic and health behaviour data was administered to a sample of cancer patients within 1 year of their cancer diagnosis at a cancer care center in Darwin, Northern Territory. Clinical data were collected via medical chart review. Results: In 63 participants (median age 58 years; 32% Indigenous), most met guidelines for fruit (73%), red meat (76%), and alcohol (75%) intake, whereas adherence to vegetable (10%), processed meat (33%), and physical activity (25%) guidelines was low. Almost one-third (32%) reported being current smokers. Indigenous patients more frequently met guidelines than non-Indigenous patients for fruit intake and physical activity, and less for vegetable, red and processed meat, alcohol intake and smoking. Although there were some notable changes post-cancer diagnosis, about half of the participants reported no change in most behaviors. Conclusions: Adherence to some recommended health behaviors was poor in this sample, regardless of Indigenous status, reflecting previous cancer survivorship and general national reports. For some this may be due to changes resulting from their cancer and treatment, whereas for others this may reflect pre-diagnosis low adherence. Further investigation is needed to better understand health behaviour decision-making for cancer patients and survivors in Northern Territory, to inform public health messaging and interventions for this population.

Systematic review of context-aware digital behavior change interventions to improve health.

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors.

Associations between Changes in Health Behaviours and Body Weight during the COVID-19 Quarantine in Lithuania: The Lithuanian COVIDiet Study.

The COVID-19 quarantine has caused significant changes in everyday life. This study aimed to evaluate the effect of the quarantine on dietary, physical activity and alcohol consumption habits of Lithuanians and the association between health behaviours and weight changes. An online cross-sectional survey was carried out among individuals older than 18 years in April 2020. The self-administered questionnaire included health behaviour and weight change data. Altogether 2447 subjects participated in the survey. Almost half of the respondents (49.4%) ate more than usual, 45.1% increased snacking, and 62.1% cooked at home more often. Intake of carbonated or sugary drinks, fast food and commercial pastries decreased, while consumption of homemade pastries and fried food increased. A decrease in physical activity was reported by 60.6% of respondents. Every third (31.5%) respondent, more often those already with overweight, gained weight. Multivariate logistic regression analysis showed that the higher odds of weight gain were associated with females, older age, increased consumption of sugary drinks, homemade pastries and fried food, eating more than usual, increased snacking, decreased physical activity and increased alcohol consumption. Our data highlighted the need for dietary and physical activity guidelines to prevent weight gain during the period of self-isolation, especially targeting those with overweight and obesity.