Determinants of accuracy in estimating the weight and volume of commonly used foods: A cross‐cultural comparison

Hébert, James R.; Gupta, Prakash C.; Bhonsle, R. B.; Verghese, Florence; Barrow, Roxanne Y.; Ellis, Sarah; Ma, Yunsheng

doi:10.1080/03670244.1998.9991561

Cited by 8 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As with our previous work (Hebert et al, 1998b(Hebert et al, , 1997, there was no effect of sequence, indicating that individuals were trained in this method of reporting very quickly. The fact that these individuals also did a good job of food size estimation (Hebert et al, 1999a) would tend to decrease overall error.…”

Section: Variability In Dietary Intake In Rural Indiamentioning

confidence: 99%

Sources of variability in dietary intake in two distinct regions of rural India: implications for nutrition study design and interpretation

et al. 2000

View full text Add to dashboard Cite

Objective: Limited inter-person variability in nutrient intake hampers epidemiologic studies of diet ± disease relationships. Despite conjecture about non-traditional study bases providing large inter-person differences, virtually nothing is known about variations in nutrient intake outside of Europe and North America. The current study was conducted in India to determine sources of variability in the intakes of nutrients thought to be of public health importance. Design: Adult subjects in Gujarat (North India; n 60) and in Kerala (South India, n 60) were administered 24 h diet recall interviews six and eight times, respectively, over a 1 y period. To assess subject (inter-person) and residual (intra-person) contributions to variance, regression models were ®t to the data. From this, the variance ratio (VR, total within or intra-person: total between or inter-person) was computed. Setting: Rural communities in North and South India. Results: In both regions, inter-person variability was larger than that observed in the West. This was most pronounced in Gujarat, for which the VR was`1.0 for seven of the 12 energy-adjusted nutrient intakes in both men and women. In analyses of the combined data, region accounted for b 20% of variance for fat, iron, copper, zinc, b-carotene and ribo¯avin in both men and women. With the region term removed from this model, virtually all of the variability ascribed to region contributed to inter-person variability. Conclusions:The relatively large inter-person variability observed here could contribute to improved`resolution' of diet ± outcome relationships in epidemiologic studies. While this applies to data from each region, it was especially evident in analyses of the combined data.

show abstract

Section: Variability In Dietary Intake In Rural Indiamentioning

confidence: 99%

Sources of variability in dietary intake in two distinct regions of rural India: implications for nutrition study design and interpretation

et al. 2000

View full text Add to dashboard Cite

show abstract

“…Also, it appears that these rural Indians may be relatively adept at estimating food portion sizes. In a separate study 13 comparing the abilities of rural Indians (including these subjects) and middle-class Americans from Massachusetts to estimate weights and volumes of common food items we observed that the differences between the Indians' estimates of food portion size and the actual values were significantly closer to zero than the differences observed in Americans. Across all three Indian study sites, relative differences for small (ϳ 50 g) and large (ϳ 300 g) items averaged 0.30 (i.e.…”

mentioning

confidence: 90%

Development and testing of a quantitative food frequency questionnaire for use in Gujarat, India

Hébert

Gupta

Bhonsle

et al. 1999

Public Health Nutr.

View full text Add to dashboard Cite

Objective: To develop and test a quantitative, interviewer-administered food frequency questionnaire (FFQ) to ascertain nutrient intakes of individuals in northern India. Design: A 92-item FFQ was developed based on food use and market surveys of the study area. A validation study was conducted consisting of 24-h diet recalls (24HR) administered on 6 randomly selected days over 1 year. Two FFQs were administered, one each at the beginning and end of the 1-year period. FFQ and 24HR-derived nutrient scores were compared using correlation and regression analyses and by computing differences between nutrient intakes estimated by the two methods. Setting: Rural villages in Bhavnagar District, Gujarat, North India. Subjects: 60 individuals who agreed to provide all necessary data. Results: Pearson (parametric) correlation coefficients averaged 0.69 in comparing nutrient scores derived from the 24HR with those from the first FFQ and 0.72 in comparing the second FFQ (P < 0.0001). Spearman correlation coefficients were virtually identical to the Pearson correlations, averaging 0.68 and 0.72, respectively. In regression analyses, most coefficients were close to 1.0 (perfect linear association). Nutrient scores were significantly and consistently higher on both FFQs relative to the 24HR. Conclusions: This FFQ produces results broadly comparable, and superior in some respects, to those commonly used in the West. Higher than average measures of association indicate its suitability for comparing exposures within this study population in reference to health-related endpoints.

show abstract

“…However, it remains difficult to record all the diversity of food sourced off-farm, with sources from many locations; wild harvested, gifts from relatives, food eaten at markets, in restaurants, etc. (Hebert et al, 1998;Deaton and Grosh, 2000;Kolodziejczyk et al, 2012). Water used for drinking was not recorded, and as water is potentially a good source of calcium (WHO, 2009a), when the recommended 1.5 litres per day are consumed, this might explain the low values for calcium seen in Figure 4.3.…”

Section: Discussionmentioning

confidence: 99%

Exploring and assessing trade-offs, synergies and diversity for smallholder agriculture

Timler¹

View full text Add to dashboard Cite

Introductionof farmers that can then be modelled, as creation of fewer working models is more efficient when scaling out technologies that impact different farmers differently. This aids in making tailor made interventions for smallholders.Participatory modelling, using the model FarmDESIGN, provides farmers insight and informs researchers of farmers' experiences, producing feasible modelling outcomes and affordances that display synergies between the participants and the researcher (Ditzler et al., 2018). Cycles of inclusive participatory co-learning along the INDEED cycle enable farmers to plan their trajectories and strategically implement changes.To assess the diversity of strategies that smallholder farmers could use, and to aid formation of homogenous participatory groups of farmers, Q Methodology can be used. Q Methodology explains diversity by clustering like-minded individuals who feel similarly about a certain topic (Stephenson, 1935). Since its inception in the second half of the 20th Century, it has been increasingly used to examine smallholder farmer perceptions, for example in the recent studies by Dingkhun et al. (2020) and Góngora et al. (2019). Dingkhun et al. (2020 show how Q Methodology can be used to define farmers' demands for soil functions and how this clustering allows community-led transition pathways to be explored. Góngora et al. (2019) demonstrate the use of Q Methodology to elucidate farmer trajectories and the drivers of these trajectories. Bringing farmers together and supporting them in groups also aids in mutual support through labour exchanges to implement innovations. This then in turn, further fosters adaptions and further experimentation by these farmer groups. 15Chapter 1: Introduction in the context of the formulated hypothesis-based approach for typology construction in chapter 2. Comparison of trade-offs faced by different farm types at the farm scaleData from the typologies performed above was used to select representative farmers in each of the three districts (n=15) in the eastern province of Zambia to be visited for a detailed characterization. A semi-structured survey was used to collect detailed farm data. These farming systems were constructed in the model FarmDESIGN. Expert knowledge from local stakeholders created the basis upon which the solution spaces for the different farm types, which were explored using a Pareto-based multi-objective optimization algorithm (Groot et al., 2012). The outer surfaces of the solution spaces represent the trade-off areas. These trade-off areas are compared between farm types and presented in chapter 3. Exploring trade-offs between improved human nutrition and farmer livelihoodsFour contrasting farms in Kenya and Vietnam along a gradient of market orientation were modelled using data collected in semi-structured interviews and through FGD's 4 , 24hr Recalls and FFQ 5 . Assessment of the nutrition status of the farm households was done using the nutrition indicators in FarmDESIGN for the households. Scenario explorations were perfor...

show abstract

Determinants of accuracy in estimating the weight and volume of commonly used foods: A cross‐cultural comparison

Cited by 8 publications

References 31 publications

Sources of variability in dietary intake in two distinct regions of rural India: implications for nutrition study design and interpretation

Sources of variability in dietary intake in two distinct regions of rural India: implications for nutrition study design and interpretation

Development and testing of a quantitative food frequency questionnaire for use in Gujarat, India

Exploring and assessing trade-offs, synergies and diversity for smallholder agriculture

Contact Info

Product

Resources

About