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Table 2 Customer recall of Eat Well @ IGA project components (n = 500), October 2017

From: The ‘Eat Well @ IGA’ healthy supermarket randomised controlled trial: process evaluation

Project component recall (estimated percentage [95%CI]) Overall a (n = 500) Customer demographic subgroups b
Age Gender SEIFA Educational attainment Store shopping frequency
18-54y (n = 172) 55y and older (n = 324) Male (n = 137) Female (n = 357) Low (n = 213) High (n = 285) High school or lower (n = 327) University or higher (n = 165) Regular (n = 136) Infrequent (n = 235)
Eat Well @ IGA overall 33 [23, 44] 37 [26, 49] 31 [22, 41] 25 [16,37] 36 [26,48] 35 [24,48] 31 [21,43] 32 [22,42] 37 [26,49] 35 [24,49] 25 [15,39]
Trolley and/or basket signs 44 [33,56] 50 [37,63] 42 [31,54] 41 [28,55] 46 [35,58] 38 [24,54] 49 [34,64] 42 [32,54] 48 [36,61] 49 [34,64] 36 [23,53]
HSR shelf tag 48 [40,57] 56 [45,66] 45 [36,54] 43 [32,54] 50 [41,59] 44 [32,57] 51 [40,62] 47 [39,56] 50 [40,61] 52 [40,63] 43 [31,56]
Posters 49 [37,61] 64 [51,76] 42 [30,55] 41 [28,56] 52 [39,65] 50 [35,65] 52 [35,62] 47 [35,59] 55 [41,68] 56 [43,69] 45 [32,60]
Shelf signs 37 [30,43] 45 [37,54] 33 [27,40] 30 [22,39] 40 [33,47] 35 [27,45] 37 [30,45] 36 [29,43] 39 [31,48] 41 [32,50] 30 [21,40]
Letterbox flyers 14 [10,17] 13 [9,20] 13 [10,18] 12 [8,20] 14 [10,19] 12 [7,20] 14 [10,21] 14 [10,18] 13 [8,19] 16 [9,26] 6 [2,13]
Staff t-shirts 24 [15,37] 26 [15,41] 24 [14,37] 23 [13,38] 25 [15,38] 23 [13,39] 25 [14,39] 25 [15,39] 23 [13,37] 28 [18,40] 25 [15,38]
Social media 12 [7,18] 13 [7,21] 11 [7,18] 10 [6,19] 13 [8,19] 14 [9,24] 10 [6,16] 12 [8,19] 10 [6,18] 13 [9,18] 12 [8,19]
  1. All questions scored as Yes/No recall of components. Bolding indicates significant difference (p < 0.05) between subgroups. HSR Health Star Rating; IGA Independent Grocers of Australia; SEIFA Index of Relative Socio-Economic Disadvantage- Socio-Economic Indexes for Areas. a Estimated using logistic mixed-effects models with store as random effect to account for the clustering induced by store. b Estimated using logistic mixed-effects logistic models with the demographic variable as a fixed effect and store as random effect.
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