Studies involving direct observation |
Hudson 1995 [9] Gambia | Repeat cross sectional Rural African community | Unclear | Phase 1: 208 ‘sinkiros’ (cooking unit within family structure) Phase 2: 12 families Phase 3: 7 males | Phase 1: All ingredients identified and weighed before being cooked | Direct observation: Each bowl weighed (1) when empty (2) after staple food added (3) after sauce added. Age, sex, body weight and amount of food waste was recorded for each participant. Phase 3: DLW study | 1. Detailed observation and measurement of meal preparation to calculate nutrient intake from each meal. 2. Average weights of staple foods/sauces consumed. 3. Energy intake estimations from two main meals |
Shankar et al. 1998 [15] Nepal | Case Control Sarlahi district—rural Nepal. 3 village development communities | Children 1-6yrs at risk of Vit A deficiency | 162 households (81 case/ 81 control) Gender NR | Direct observation by 10 local Nepalese males trained for 3 months | Direct observation of control and case participants | 1. Classification of feeding episodes: no food sharing/shared plate eating/interplate sharing 2. Shared plate vs individual plate eating 3. Average portion sizes 4. Odds of consuming different food groups by feeding type |
Shankar et al. 2001 [14] Nepal | Validation study as part of larger longitudinal study Sarlahi District. Rural region of Nepal | Children aged 1–10 years old. | 11 (6 male, 5 female) 17 field tests (9 individual plate, 8 shared plate setting) | Direct observation by 8 observers who undertook 3 months of training | Food weighing used as reference to determine accuracy of observers’ visual direct observation of food intake. | 1. Accuracy of observations in individual plate eating and shared plate eating 2. Comparison of estimates between observers |
Studies using 24 h recall dietary assessment method |
Abu-Saad et al. 2009 [20] Israel | Cross sectional Semi nomadic population in Southern Israel | Healthy 19–82-year-old semi-nomadic adults visiting hospital patients or attending Maternal and Child Health Care clinics | n = 451 (149 male, 302 female) >1× 24HR recall. 40 completed 3 × 24HR recalls | Modified USDA 24HR recall conducted by trained interviewers and administered using the multi-pass method | EI calculated using American Food Information Analysis System. Compared EI from 24HR recall with BMR using the Schofield equation | 1. Eating patterns 2. Nutrient intakes 3. EI using Schofield vs recall 4. Day to day variation in 3-day results for 40 respondents |
Caswell et al. 2015 [19] Zambia | Cross- sectional | Children aged 4–8 not yet enrolled in school | 938 (479 male, 459 female) | 24 h recall conducted on tablet by local interviewers | Nutrient intakes were calculated using food composition tables developed for Zambia by HarvestPlus. USDA National Nutrient Database and other local food composition tables. | 1. Demographic Characteristics 2. Common foods consumed 3. Nutrient intakes |
Savy et al. 2005 [11] 2007 [12] Burkina Faso | Cross sectional | Women living in randomly selected compounds with at least 1 child under 5 years of age. | 691 females | Three-day dietary intake 24 h recall conducted by 14 local fieldworkers. Food variety score (FVS) and Dietary Diversity Score (DDS) calculated | NR | 1. Relationship FVS + DDS and socio-demographic and economic characteristics 2. Relationships between DDS + FVS and anthropometry 3. Relationship between DDS + FVS and nutritional status |
Studies using an interview or questionnaire method |
Daniel et al. 2014 [16] India | Cross sectional 3 regions of India; New Delhi, Mumbai and Trivandrum. Selected to capture cancer registries | Aged 35–69 years old, resided in study area for at least 1 year. | 3908 (male and female) completed DHQ, 3862 included in analysis after data cleaning | Interviews conducted by trained staff at home using New Interactive Nutrition Assistant–Diet in India Study of Health (NINA-DISH): (1) DHQ, (2) questions on meal times; (3) food-preparer QA and (4) 24HR recall | NR | 1. Number of food items from food groups reported in DHQ & 24HR recall 2. Number of total food items and time taken to complete DHQ & 24HR recall 3. Top food contributors to nutrient values |
Ferrucci et al. 2010 [17] India | Cross sectional from national registry (cancer specific content) three regions (New Delhi, Mumbai and Trivandrum) | Aged 35–69 years old, resided in study area for at least one year. Recruited one male and one female/household | 3625 (male and female) (New Delhi n = 835, Trivandrum n = 2,044, Mumbai n = 746) | Computer-based diet QA using NINA-DISH software administered by trained field personnel | NR | 1. Global spice consumption and cancer incidence 2. Consumption of spices and seasonings in participants 3. Consumption of commonly used cooking oils 4. Socio-demographic characteristics |
Iwaoka et al. 2001 [21] Japan | Cohort College | Dietetics students and their mothers | 64 females (32 households) | Approximated proportion | Individual-based food weighing method | 1. Mean difference energy and nutrient intakes between methods |
Studies using dietary assessment tools of interest to shared plate eating |
Jerome 1997 [13] Egypt and Grenada | Case Study NR | Egypt: Kalama village, periurban community. Grenada | NR | Egypt: Household and individual intake, Grenada: Dietary information reported from each individual in the household (not shared plate) | NR | To use both case studies to highlight the importance of matching the dietary assessment method with the culture of the population being studied. |
Thoradeniya et al. 2012 [18] Sri Lanka | Cross sectional Laboratory | School children 10–16 years | 80 (32 male, 48 female) | Portion size estimation aids of 16 food items: (1) small photographs (n = 11 foods, 876 estimations), (2) life-size photographs (n = 7 foods, 558 estimations), (3) 2D life-size diagrams (n = 16 foods, 1271 estimations) and (4) household utensils (n = 6 foods, 475 estimations) | Actual weight of food | 1. Precision and accuracy or portion size estimations tools for Asian Countries |