Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women's Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda.
Andrea L S BulunguLuigi PallaJan PriebeLora ForsythePamela KaticGwen VarleyBernice D GalindaNakimuli SarahJoweria NamboozeKate WellardElaine L FergusonPublished in: Nutrients (2022)
Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women's time use in rural Eastern Ugandan. Women's ( n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland-Altman limits of agreement (LOA) method of analysis and Cronbach's coefficient alpha (time allocation) or Cohen's κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (-1.1 to 3.7) and AWC-IAR (-3.2 to 3.2). Cronbach's alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women's time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women's time allocations and nutrition outcomes.
Keyphrases
- polycystic ovary syndrome
- pregnancy outcomes
- south africa
- cervical cancer screening
- deep learning
- machine learning
- physical activity
- metabolic syndrome
- heart rate
- type diabetes
- pregnant women
- magnetic resonance
- adipose tissue
- blood pressure
- high throughput
- systematic review
- skeletal muscle
- radiation therapy
- single cell
- convolutional neural network
- weight loss
- birth weight