Obesity is a global health concern and a health priority in the United Kingdom. Overall, 13 participants (n=11, 85% women and n=2, 15% men) and two health care professionals were interviewed about their experience of using the avatar program. Quality-of-life and self-efficacy measures demonstrated greater improvement in the intervention arm at both T1 (105.5 for routine care arm and 99.7 for intervention arm) and T2 (100.1 for routine care arm and 81.2 for intervention arm). Mean change in participants’ weight from T0 to T2 was 4.5 kg (95% CI 2.7-6.3) in the routine care arm and 5.3 kg (95% CI 3.9-6.8) in the intervention arm. Participants’ initial mean weight was greater in the intervention arm than in the routine care arm (126.3 kg vs 122.9 kg) pattern of weight loss was similar across both arms of the study in T0 to T1 period but accelerated in T1 to T2 period for intervention participants, suggesting that access to the self-resembling avatar may promote greater engagement with weight loss initiatives in the short-to-medium term. Variables where the answer choices are infinite – such as Text Entry fields – are not compatible.Overall, 10 men (n=7, 70% for routine care and avatar and n=3, 30% for routine care) and 33 women (n=23, 70% for intervention and n=10, 30% for routine care) were recruited. Embedded Data set to Number Set or Text Set type.Start Date or End Date by Day, Week, Month, Quarter, or Year.Hot Spots and Heat Maps with defined regions.Single answer multiple choice questions.Hover over the top left corner so that the blue scale icon appears.Īny variable with a finite number of answer choices can be supported by response weighting.Turning off Weighting for a Visualization At this time, new Results dashboards do allow you to customize your report’s weighting. Qtip: This section refers to the legacy Results reporting feature. Characteristics for minimum weight are the variable combinations corresponding to the minimum weight. Each combination of variables has a corresponding weight. Characteristics for minimum weight X: X varies based on what your minimum weight is.Weights greater than 1: Of all the characteristics you’re weighting, this is the number of characteristics that have a multiplier greater than 1.Weights less than 1: Of all the characteristics you’re weighting, this is the number of characteristics that have a multiplier less than 1.This is represented as a multiplier instead of a percentage out of 100. Weight Efficiency: This section shows you how efficient your weighting scheme is.As more variables are added, the balance can be driven down when weights are extreme (having very large or very small values), impacting overall weighting efficiency. Sample Balance: The Sample balance is a measurement of weighting efficiency during rake weighting convergence.Your Cardinality limit is then 350 since that is the product of the number of categories (10 x 5 x 7 = 350). Once you’re finished, click Save weights.Įxample: For example, let’s say you are weighting three variables with 10, 5, and 7 categories respectively.If you need to make changes, click Back.Read the Weight report for more information about your weighting scheme.
Review your weights to make sure everything is correct.
Visualize you weight change viewer update#
The weight report will also update to show you information about your weighting scheme. Your weights will now be calculated, which may take up to a couple minutes. You can add variables to your weighting scheme until the Cardinality Limit reaches 5,000.
As you add weights, the weight report will adjust to show you your Cardinality Limit.If you want to remove a variable from your weighting, click the X next to the variable name above your weighting scheme to remove the variable.See Weight Reports for more information about Cardinality Limits. For example, if your first wave has a cardinality of 100 and your second wave has a cardinality of 200, your total cardinality is 300. Qtip: You can add variables until the total Cardinality Limit of your weighting reaches 5,000.
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