With a new function in KIWI HEALTH, you can now display an additional health value directly in the chart – and analyze your health data more effectively.<\/strong><\/p>\n\n Compare health data: What initially seems like a small extension opens up completely new possibilities in practice.<\/strong><\/p>\n\n You may have experienced this yourself.<\/p>\n\n You look at your resting heart rate and notice that it was elevated in certain weeks. But what was the cause?<\/p>\n\n Was it less sleep? Health data becomes truly meaningful when relationships between values become visible. This is exactly where the new comparison feature comes in. <\/p>\n\n With the latest update of KIWI HEALTH, you can also display a freely selectable second value in the diagram when evaluating your health data (you can find out what else diagrams in KIWI HEALTH can do in the article The power of diagrams…)<\/a>.<\/p>\n\n The selection is made very simply via a dropdown menu. The selected health value is added immediately – without existing settings being lost. <\/p>\n\n The following applies:<\/p>\n\n This means, for example: As usual, you can switch between different time groupings:<\/p>\n\n The selected aggregation is applied to both values automatically. This allows you to analyze short-term details just as easily as long-term trends. <\/p>\n\n Even with large amounts of data – such as continuously recorded heart rate values – the display remains performant and responsive. During development, we deliberately ensured that the analysis works smoothly even with large data sets. <\/p>\n\n One important aspect when analyzing multiple health values is scaling.<\/p>\n\n Body Weight is measured in kilograms, step counts in numbers, and heart rate in beats per minute. If both values were forced onto the same scale, visual distortions would occur. <\/p>\n\n This is why each health value in the diagram has its own Y-axis. This means that the orders of magnitude are correct and the representation remains meaningful. <\/p>\n\n This clean separation ensures that you can really analyze your health data – instead of just seeing it side by side.<\/p>\n\n With this new feature you can explore questions such as:<\/p>\n\n Do body weight and calorie consumption develop in parallel? Such comparisons help you to recognize patterns and verify assumptions.<\/p>\n\n You may notice that changes in weight follow changes in calorie balance with a certain delay. Or that your resting heart rate rises measurably after weeks of intensive training. <\/p>\n\n Visualizing both values in the same chart makes these relationships much easier to recognize.<\/p>\n\n Another advantage: This includes:<\/p>\n\n\n\n This allows you to document, archive or pass on your analysis – without the display changing. This reproducibility is a decisive advantage, especially if you want to evaluate your health data over the long term or discuss it with medical professionals. <\/p>\n\n With this enhancement, KIWI HEALTH takes a further step towards structured health analysis. Instead of looking at individual values in isolation, you can now compare specific health values, visualize correlations and better understand developments. <\/p>\n\n The combination of freely selectable time aggregation, independent Y-axes, different series types, high-performance display and complete PDF export turns a diagram into a real analysis tool.<\/p>\n\n Collecting health data is now easier than ever – whether through Apple Health<\/a> or Google Health Connect<\/a>.<\/p>\n\n However, the real added value only arises through targeted evaluation. Only when you compare values with each other do patterns become visible and developments comprehensible. <\/p>\n\n With the new comparison feature in KIWI HEALTH, you can analyze two health values within a single chart and directly compare their development over time. This gives you a clearer picture of your personal relationships – and allows you to classify changes more consciously. <\/p>\n\n Because in the end, it’s not about individual numbers.![\"Display](\"https:\/\/kiwi-health.de\/wp-content\/uploads\/2026\/03\/Zwei_Gesundheitswerte_anzeigen-1024x666.webp\")
<\/figure>\n\nWhy looking at a single value is often not enough<\/h4>\n\n
Display two health values in one diagram<\/h4>\n\n
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Flexible time aggregation \u2013 from raw data to long-term trends<\/h4>\n\n
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Methodically correct: Two Y-axes<\/h4>\n\n
Practical examples for analyzing health data<\/h4>\n\n
The PDF report reflects exactly what you see<\/h4>\n\n
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<\/figure>\n<\/div>\n<\/div>\n\nFrom visualization to analysis<\/h4>\n\n
Conclusion<\/h4>\n\n