Older adults' compliance with mobile ecological momentary assessments in behavioral nutrition and physical activity research: pooled results of four intensive longitudinal studies and recommendations for future research.

BACKGROUND: Mobile Ecological Momentary Assessment (EMA) is increasingly used to gather intensive, longitudinal data on behavioral nutrition, physical activity and sedentary behavior and their underlying determinants. However, a relevant concern is the risk of non-random non-compliance with mobile EMA protocols, especially in older adults. This study aimed to examine older adults' compliance with mobile EMA in health behavior studies according to participant characteristics, and prompt timing, and to provide recommendations for future EMA research. METHODS: Data of four intensive longitudinal observational studies employing mobile EMA to understand health behavior, involving 271 community-dwelling older adults (M = 71.8 years, SD = 6.8; 52% female) in Flanders, were pooled. EMA questionnaires were prompted by a smartphone application during specific time slots or events. Data on compliance (i.e. information whether a participant answered at least one item following the prompt), time slot (morning, afternoon or evening) and day (week or weekend day) of each prompt were extracted from the EMA applications. Participant characteristics, including demographics, body mass index, and smartphone ownership, were collected via self-report. Descriptive statistics of compliance were computed, and logistic mixed models were run to examine inter- and intrapersonal variability in compliance. RESULTS: EMA compliance averaged 77.5%, varying from 70.0 to 86.1% across studies. Compliance differed among subgroups and throughout the day. Age was associated with lower compliance (OR = 0.96, 95%CI = 0.93-0.99), while marital/cohabiting status and smartphone ownership were associated with higher compliance (OR = 1.83, 95%CI = 1.21-2.77, and OR = 4.43, 95%CI = 2.22-8.83, respectively). Compliance was lower in the evening than in the morning (OR = 0.82, 95%CI = 0.69-0.97), indicating non-random patterns that could impact study validity. CONCLUSIONS: The findings of this study shed light on the complexities surrounding compliance with mobile EMA protocols among older adults in health behavior studies. Our analysis revealed that non-compliance within our pooled dataset was not completely random. This non-randomness could introduce bias into study findings, potentially compromising the validity of research findings. To address these challenges, we recommend adopting tailored approaches that take into account individual characteristics and temporal dynamics. Additionally, the utilization of Directed Acyclic Graphs, and advanced statistical techniques can help mitigate the impact of non-compliance on study validity.

Inequality in physical activity, global trends by income inequality and gender in adults.

BACKGROUND: Physical inactivity is a global pandemic associated with a high burden of disease and premature mortality. There is also a trend in growing economic inequalities which impacts population health. There is no global analysis of the relationship between income inequality and population levels of physical inactivity. METHODS: Two thousand sixteen World Health Organisation's country level data about compliance with the 2010 global physical activity guidelines were analysed against country level income interquantile ratio data obtained from the World Bank, OECD and World Income Inequality Database. The analysis was stratified by country income (Low, Middle and High) according to the World Bank classification and gender. Multiple regression was used to quantify the association between physical activity and income inequality. Models were adjusted for GDP and percentage of GDP spent on health care for each country and out of pocket health care spent. RESULTS: Significantly higher levels of inactivity and a wider gap between the percentage of women and men meeting global physical activity guidelines were found in countries with higher income inequality in high and middle income countries irrespective of a country wealth and spend on health care. For example, in higher income countries, for each point increase in the interquantile ratio data, levels of inactivity in women were 3.73% (CI 0.89 6.57) higher, levels of inactivity in men were 2.04% (CI 0.08 4.15) higher and the gap in inactivity levels between women and men was 1.50% larger (CI 0.16 2.83). Similar relationships were found in middle income countries with lower effect sizes. These relationships were, however, not demonstrated in the low-income countries. CONCLUSIONS: Economic inequalities, particularly in high- and middle- income countries might contribute to physical inactivity and might be an important factor to consider and address in order to combat the global inactivity pandemic and to achieve the World Health Organisation target for inactivity reduction.

The prediction of preeclampsia: reassessment of clinical value of increased plasma levels of fibronectin.

OBJECTIVE: To determine whether assessment of plasma fibronectin in primigravidae could predict the pregnant women expected to become preeclamptic. METHODS: We performed a prospective blinded analysis of 156 apparently normotensive primigravidae in an outpatient clinic. Blood samples were taken at 6 week intervals from the 18th week and immediately after delivery or at the onset of preeclampsia. Plasma fibronectin was evaluated by ELISA. Evolution with gestational age was studied using regression curves. RESULTS: We had 148 normal primigravidae (592 determinations). In three women, increased fibronectin anticipated preeclampsia by 3-4 weeks. Five women showed high levels only at the onset of preeclampsia. Sensitivity, specificity, positive and negative predictive values of increased fibronectin levels were 37.5% (95% CI=3.3-71.7), 96.6% (95% CI=93.7-99.6), 37.5% (95% CI=3.3-71.7) and 96.6% (95% CI=93.7-99.6), respectively. CONCLUSIONS: This study shows that plasma fibronectin levels could represent a specific marker for preeclampsia. Its sensitivity has to be improved but its high negative predictive value strongly argues against the development of preeclampsia within the next 4 weeks after the blood sampling.