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Nutrition in the digital age - How digital tools can help to solve the personalized nutrition conundrum
Trends in Food Science & Technology 90 (2019) 194–200
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Commentary
Nutrition in the digital age - How digital tools can help to solve the personalized nutrition conundrum
T
M. Michel, A. Burbidge∗ Nestlé Research, Institute of Material Sciences, Department of Technology, Vers-Chez-Les-Blanc, CH-1000, Lausanne 26, Switzerland
A R T I C LE I N FO
A B S T R A C T
Keywords: Nutrition Personalization Life-style Non-communicable diseases Digital self-learning expert system Health
Background: It has become increasingly clear that the current population averaged nutrition paradigm is not able to address the growing non-communicable disease (NCD) epidemics. Current approaches fail primarily for two reasons: firstly, poor adherence to public dietary advice and, secondly, individual health responses are not well reflected by population averages, as generic public health advice lacks relevance for individuals, personally and medically. Scope and approach: Personalized ‘expert’ systems are, potentially, powerful weapons against NCDs. Existing systems are, however, handicapped by both difficulty in measuring dietary intake reliably and that of tying population level nutritional knowledge to stochastic individual responses. In order to address these shortcomings, we propose an approach that no longer distinguishes between behavioural and physiological responses. Key findings and conclusions: We outline how a conceptual self-learning expert system could implement this approach, based on multifactorial lifestyle interventions, and give specific examples in the contexts of diabetes and obesity. Combining behaviour and physiological responses into a single entity removes the requirement to measure food intake, enabling users to map their individualised ‘path of least resistance’ to specific health outcomes. This new approach could be provided at minimal cost by leveraging users existing mobile devices, e.g. smart phones, watches, fitbits etc. The novelty in this concept is that the methodology purposely does not attempt to understand the complexity of the underlying physiological, metabolic and psychological responses. Despite requiring scientifically validated biomarkers, understanding the discrete influences of each of these factors is not required to drive improved individual-level outcomes.
1. What does not work in today’s nutrition paradigm? The definition of essential nutrients and nutrient requirements scientifically underpins nutrient-based dietary advice. There are, however, obvious limitations in this reductionist approach, not least that people consume foods and not just individual nutrients. A large variety of ingredients and food products are combined in varying amounts to form diets and it is diets that are healthy or otherwise, not individual foods or ingredients. Thus, it is difficult, if not impossible, to determine the precise formulation of an individual product that can, when combined with other foods, provide nutritionally adequate diets under all circumstances. Dietary advice based on the food pyramid (USDA, 2015) have so far failed to prevent the growing rates of obesity and other nutrition-related chronic diseases (Gibney, Gossens, & Walsh, 2016). There is nothing wrong per-se with reformulation of food products, but this alone will not solve the problems associated with unhealthy dietary choices and lifestyles. Furthermore, individuals are far from
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standardized. The ability of the human body to absorb and utilize nutrients depends on various factors such as age, health, genetic make-up, personal nutritional status (e.g., those low in iron will usually absorb it more efficiently from diet – unless of course malabsorption is their base issue) and activity patterns. Differences in caloric needs of individuals are far more complex than just a function of BMI and are also strongly influenced by lifestyle (Johnstone, Murison, Duncan, Rance, & Speakman, 2005). Recent literature has demonstrated that the responses of individuals are often very different for identical nutritional stimuli, as demonstrated by, for example, postprandial glycaemia (Sonnenburg & Sonnenburg, 2015; Zeevi et al., 2015). Consequently, there is a need to tailor advice to the particular circumstances of an individual. Indeed the recently completed pan-European Food4Me project demonstrated that personalized nutrition can be feasible and effective and that the delivery of personalized advice is more effective in changing dietary intakes than general healthy eating messages (Gibney et al., 2016; Livingstone et al., 2016; Ronteltap & van Trijp,
Corresponding author. E-mail address: [email protected] (A. Burbidge).
https://doi.org/10.1016/j.tifs.2019.02.018 Received 18 July 2018; Received in revised form 8 October 2018; Accepted 6 February 2019 Available online 08 February 2019 0924-2244/ © 2019 Elsevier Ltd. All rights reserved.
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financial resources go into medical care while almost nothing is spent on improving behavioural patterns. This is unlikely to change in the short-term focused political environment. Table 1 lists some of the more obvious health influence factors (Boyd 2010). It is notable that the selection spans a range from highly modifiable through to non-modifiable health influence factors. The individual can adjust some of these, such as smoking, diet and physical activity, whereas others such as age or heredity are completely out of their control. The health influence factors can raise (e.g. a high caloric diet in combination with little or no physical activity) or reduce (e.g. eating a fibre rich diet) the risk level of an individual. Clinical testing of these health influence factors measures the effect of an imposed treatment on a particular health outcome, usually a validated biomarker such as serum glucose level, blood pressure etc. Generally, it is believed that a deeper scientific understanding of the fundamental biological mechanisms underlying different health determinants will suggest pathways to a solution. Although perfectly valid, this approach fails to take account of adherence of the free-living individual to the recommended treatment. From the perspective of outcomes, even the best dietary advice in the world is indistinguishable from the worst when the individual does not or cannot adhere to the advice due to specific circumstances, e.g. place of residence, access to healthy foods, employment conditions etc. This is the root cause of ineffectiveness in most public health campaigns. In order to address this problem, we propose a shift from measuring efficacy towards measuring the effectiveness of dietary advice on particular health target(s). The key difference is that we define efficiency as the composite effect of the treatment and adherence, rather than the effect of treatment alone. As we shall see in the following sections, this greatly simplifies our intervention strategy and opens the door to further personalization. This terminology finds a parallel in clinical trial nomenclature, where there are significant differences between efficacy and effectiveness trials. Randomized clinical trials of drugs or procedures are most often efficacy studies that are designed to show whether the drug or procedure produces the desired clinical outcome under optimal conditions. In contrast, effectiveness trials focus on whether a given drug or procedure works under usual circumstances (Kim, 2013). In clinical trials parlance, our proposed approach is a form of adaptive, continuous, ‘personalized (n = 1) effectiveness trial’.
2007). In summary, poor nutrition is a consequence of an inappropriate quantity and quality of food in the diet. The problems of diet-related NCDs cannot be addressed effectively using traditional, population level nutritional advice. As McCarthy mentions, food and health research needs to move from ‘healthy food’, which concentrates on food as a product, to research for ‘healthy eating’, which is concerned with appropriate overall food intake adapted to an individual's situation (McCarthy, Cluzel, Dressel, & Newton, 2013). In the remainder of this article, we will argue for a paradigm shift, away from the current research focus of measuring the effect of a prescribed, but often not followed, diet on population-averaged cohorts, towards measuring the efficiency of dietary and lifestyle advice for an individual. We will sketch out how recent advances in digital technologies can both enable and accelerate this change. Our objective will be to optimise the effectiveness of personalized advice; not to better understand the underlying causes of observed response. This is a crucial simplification that, as we will argue in the following sections, obviates the need to measure dietary intake. Removing this need is an important step forward, since one of the most difficult parts of developing diet management systems is in quantifying intake reliably and simply.
2. Integrating nutrition in the context of an individuals’ lifestyle Clearly, nutrition is not the only factor that determines the health and wellness of an individual. Nevertheless, it is a significant and essential part of a complex web of inter-related ‘health influence factors’ (see Table 1) that include individual behaviours, environmental factors, and heredity. The relative impact of each of these on premature death in the US is estimated as follows (McGinnis, Williams-Russo, & Knickman, 2002; McGovern, Miller, & Hughes-Cromwick, 2014):
• 30% genetic predispositions • 15% social circumstances • 5% environmental exposures • 40% behavioural patterns • 10% shortfalls in medical care These numbers demonstrate that behaviours are responsible for almost half of all premature deaths. Even this is an underestimation of the overall cost to society since no account is taken of co-morbidities (e.g. diabetes associated with obesity, muscular-skeletal issues associated with excess weight and obesity, etc.) that affect an even larger population group without necessarily killing them. Over the medium to longterm, these health-influencing factors both modulate behaviours and strongly influence health. Strictly speaking, health influence factors are stochastic variables modulating NCD risk, although the reality is slightly more complex since individuals that behave in a very risky fashion can ‘get lucky’ and vice-versa. It is notable that today, most
3. From generic dietary advice towards personal eating efficiency This section lays out a vision of how digital technologies can help individuals towards better eating habits. There are a number of potential roadblocks to implementation such as identifying scientifically sound target variables, engagement of the consumer in a sustainable way and managing data overload and trust. Discussion of these issues will be postponed until section four.
Table 1 Modifiable and non-modifiable ‘health influence factors’. Adapted from (Boyd, 2010).
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3.1. What does personalized nutrition mean?
the following steps (see Fig. 3):
As outlined by Ordovas and co-workers (Ordovas, Ferguson, Tai, & Mathers, 2018), personalized nutrition pursues the idea that individualising nutritional advice, products, or services will be more effective than approaches based on generic advice. There are two aspects that need to be addressed: 1) physiological responses to foods/nutrients, and, 2) individual behavioural patterns including, preferences, barriers, and motivations. The recent Food4me program provides evidence that internet delivery of personalized nutrition is more effective than traditional generic dietary advice. Furthermore, the inclusion of genetic data did not appear to add additional benefits (Livingstone et al., 2016). These results encourage further personalized intervention. Up until the time of writing, most research has tended to focus on either the medical or the behavioural aspects of the problem in isolation. In the remainder of this paper, we will argue that there are potential benefits to combining these two approaches using digital devices. Mobile devices are now ubiquitous in the developed world and are rapidly becoming available everywhere. Many cheap portable sensors that interface with these devices are also emerging. Taken together with data connectivity, the internet of things and validated nutritional databases they provide an excellent foundation for the development of personalized self-learning systems. An important advantage of such digital expert systems is their capacity to take account a multitude of personal data channels, such as dietary habits including personal preferences, health status, physical activity, sleep patterns, personal value landscape, etc. As Gibney and co-workers mention (Gibney et al., 2016), analysing and combining data from many different channels is likely to add the most value for managing the health and well-being of individuals. In our new approach, the basic requirement for a digital expert system is to measure the effectiveness of dietary or lifestyle advice, e.g. how the device owners plasma glucose level changes as a function of an advice (e.g. try to avoid sugar rich beverages). From a concrete perspective, this requires that both the sensitivity (gain) and directionality (positive or negative) of advice (e.g. example for diabetes in Table 3) on chosen target(s) (Fig. 2) be quantified. Mathematically speaking, the system attempts to quantify the mapping between the advice (input variables) and outcomes (response variables). In a linear system, the advice and the outcomes are both vectors, and the mapping is a personalized transfer function matrix (see Fig. 1). In a self-learning system, the personalized mapping evolves over time, based on measured responses to advice. For our purposes, we need fast variables for two reasons – (1) learning rate of the system itself for the individual is directly proportional to how long it takes to ‘explore the space’ (2) maintaining the user's attention and providing positive reinforcement feedback that the intervention is having some positive effect as illustrated in Fig. 2. How could/would such a system operate? The concept is based on a classical feedback controller model, as used for over a century in a broad range of applications, and involves
1) identify what to improve (see Fig. 2). 2) set target e.g. validated biomarker (see Table 2) and initialize personalization matrix using state of the art population based nutrition knowledge 3) measure - ideally non-invasive 4) determine deviation from target (delta) 5) provide advice e.g. specific products to avoid/to eat, meal & training plan etc. (see Table 3) 6) learn about progress towards set target(s). Update ‘Personalization’ matrix. 7) optional: individual gives input on determinants/advice used - if available will speed up learning process Progress around this ‘closed loop’ (step 5 loops back to step 3 and repeats) over a period of time will evolve a specific set of mappings between advice and the defined target. In other words, the system learns what is easy for the individual to do in order to achieve set target (s) (finding a personal ‘path of least resistance’). This set of mappings will be unique to the individual and encapsulate their personal map of constraints and effective modulators. Since this map (personalization matrix) only needs to be stored on the user's device, they own the data but could also opt to share the data on a voluntary basis. Data obtained from a cohort of users would be of value to researchers and companies to stratify sub-groups of consumers. Such stratification would be particularly useful for improving the system's speed of learning since there are likely to be common features between certain sub-groups. Starting with the best available nutritional knowledge (Asif, 2014; Fern, Watzke, Barclay, Roulin, & Drewnowski, 2015; Ley, Hamdy, Mohan, & Hu, 2014) is likely to be very important, since we know that people have limited patience if they do not see a benefit. Furthermore, gaps in the market (e.g. breakfast cereals with low sugar content), could be identified in this way, which could stimulate food producers to develop better product offerings for particular consumer groups. Choosing an appropriate target and associating it to a suitable control variable is a prerequisite to implement the above scheme. Fig. 2 gives some examples of target choices, which fall broadly into two categories: (i) validated biomarkers, (ii) subjective self-assessments. The first category are generally measurable in an objective quantitative fashion and relate to specific medical conditions. The second group covers the wider area of ‘wellness’ and ‘wellbeing’, so in this case are directly perceived by the user. This difference in the nature of target variables has a very important influence on the psychological compliance aspect of device/advice taking. In the helicopter view, the medical biomarkers are long-term preventative, in contrast to the selfassessments, which are short-term ‘rewards’. Balancing ‘reward’ and ‘prevention’ is an emerging opportunity, which we will discuss further in section 4. 3.2. Example risk factor diabetes In the current context, diabetes can be viewed as an inability to manage plasma glucose concentration in response to nutrient intake. Of particular interest are pre-(type II) diabetic people, who show the first indications of inadequate plasma glucose response (i.e. poor glucose tolerance), which can still be controlled through nutritional interventions. From the perspective of the control system, the first step is to identify a suitable control variable (Table 2 lists some options). An effective control variable needs to be both coupled to the desired outcome, and exhibit a relatively rapid response to nutrient intake (i.e. biological system response time is comparable to the timescale of eating events). From this perspective, the ideal control variable would appear to be plasma glycaemia itself. However, diagnosed type II diabetics are likely to be taking medication to control plasma glycaemia in which case the glycaemia itself would probably not be a good indicator. There
Table 2 Biomarkers for individuals with diabetes adapted from (Gray, 2015). DIABETES IndicatorS
Target range
Speed of response
A1C Blood Pressure LDL Cholesterol Triglycerides HDL Cholesterol
< 7% < 140/80 mm Hg < 100mg/dL < 150mg/dL > 40mg/dL (Men) > 50mg/dL (Women) 20–25 < 7.8mmol/L
Slow (months) Slow (with fast fluctuations) Slow Slow (with fast fluctuations) Slow Slow Slow Fast (Minutes)
Body Mass Index Plasma Glycemia
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Table 3 Advice for diabetes prevention adapted from: (Asif, 2014; Ley et al., 2014), https://www.hsph.harvard.edu/nutritionsource/disease-prevention/diabetesprevention/preventing-diabetes-full-story/#weight-control. DIET ADVICE
OTHER ADVICE
Focus on plant foods to increase fibers: Choose whole grains and whole grain products over highly • processed carbohydrates. Cut back on refined carbs and sugary drinks. White bread, white rice, white pasta and potatoes cause • quick increases in blood sugar, as do sugary soft drinks, fruit punch, and fruit juice. Over time, eating
• • • •
lots of these refined carbohydrates and sugar may increase your risk of type 2 diabetes. To lower your risk, switch to whole grains and skip the sugar, especially the sugary drinks, and choose water, coffee, or tea instead. Choose healthy fats and proteins, and skip the red and processed meat. A diet rich in mono and polyunsaturated fats can help lower your risk of diabetes and heart disease. Canola oil and olive oil are great choices, as are the fats in avocados, nuts, and seeds. Limit red meat and avoid processed meat; choose nuts, whole grains, poultry, or fish instead. Substituting low–glycemic load foods for higher–glycemic load foods may modestly improve glycemic control. Alcohol now and then may help
briskly for a half hour every day reduces the risk of • Walking developing type 2 diabetes by 30 percent. (18, 19) • Reduce watching TV
• Use stairs instead of elevators instead of driving • Walk • Go to gym • Stop smoking
Following the advice, individuals are free to act as they wish. This is somewhat counterintuitive in establishing the effectiveness of a strategy, but there is no need to measure whether or not the user takes the advice! This is an extremely important point. It is valid only because, in the new approach, we choose not to distinguish between the specific effect of a particular intervention and the adherence of the user. We only need to know how effective a particular advice was in achieving the goal; not why it did or did not work. Taking the example of glycaemia, either eating food with a low GI or a course of aromatherapy are likely to be very different from an efficacy perspective. However, if the individual does not follow the low GI advice then the effectiveness of these two recommendations are indistinguishable. Finally, the response is analysed – this is the crux of establishing whether a suggested strategy is effective or not for the individual user. Effectiveness is defined as progress of the target variable in the desired direction – i.e. did the advice influence the ‘delta’ between the set target and the measured state. There are three possible outcomes: (1) delta was reduced, in which case the advice is defined as a positive modulator with a quantifiable effectiveness, (2) delta increased, thus the advice is defined as a negative modulator (antagonist) to be avoided in this context, (3) delta was unchanged, in which case the advice is actually a constraint (i.e. a modulator with a ‘gain’ of zero). In this manner, the ‘personalization matrix’ is evolved over time (cycles around the closed loop controller) from the generic starting point to a bespoke version. This (pre) type II diabetes example is quite simple because we have a very clear, single target variable and a very rapid feedback mechanism. One of the big advantages of this approach is that there is no need to know the exact underlying physiological mechanisms for it to work. Other targets are much more complex, but the general approach remains valid. To demonstrate this, we will now discuss the more complex example of obesity reduction.
are also regulatory and potential ethical barriers to the use of an invalidated device in a medical context. For these reasons, we will focus on pre-diabetics for the remainder of this section and select plasma glycaemia as our primary response variable. Obviously slow variables such as BMI can still be reported, and improvements in these are also expected and can be monitored by the system in such a manner that they can be secondary optimisation targets. Working from Fig. 3, we have the following sequence of events: The risk is pre type II diabetes and our target is improved control of plasma glucose response. The measured variable is plasma glucose concentration itself, and there is technology available that provides a non-invasive, time continuous readout of this (e.g. FreeStyle Libre Flash glucose measuring system from Abbott) (Chen et al., 2017). The target value is that basal glucose concentration, which should be maintained below 7 mMol/L and extreme variations should be avoided, thus – from a control perspective – the aim is to minimise the dynamic plasma glucose changes. The next step is to establish the possible influence factors and constraints. It is important to understand the conceptual difference between a ‘modulator’ and a ‘constraint’, as this is key to personalizing the system. Clearly, we will have some ‘hard constraints’ (e.g. genetics), but almost everything else will appear on a spectrum between a pure ‘constraint’ and a ‘perfect modulator’ (Table 1). Individualisation of the system requires learning whether something is a constraint or a modulator for a particular individual. Some of these data are easily obtained (e.g. perhaps an individual has an allergy to a particular protein or is lactose intolerant), but many factors must be learned by the system. Existing population level nutritional knowledge is a good starting point (e.g. Table 3); in the current context, this would likely include minimising the consumption of particular foodstuffs with a high glycaemic index (GI) and/or diets with a high glycaemic load (GL). Once the system establishes the current state of constraint and has identified the most likely ‘modulators’, advice will be given to the user. In this case, typical advice might be to avoid sugary beverages (high GI and GL). Since the aim is to be self-learning, advice will differ over time in order to explore the effectiveness of various modulators and the rigidity of constraints. Essentially, the modulator-constraint space for the individual user is being explored.
3.3. Example obesity Obesity is a much looser concept than diabetes, so a more specifically quantifiable goal is required. A subtle point worth mentioning here is that the measure could also be completely subjective (e.g. how tired do I feel) for the argument to apply equally well. For the sake of Fig. 1. Schematic representation of a linear response system. The aim of the self-learning system is to systematically vary the ‘Advice Vector’ {A whilst measuring the ‘Outcome Vector’ {O} such that the ‘Personalization Matrix’ [P] is adapted to the user (++ strong positive influence, + moderate positive influence, 0 no influence, - moderate negative influence, – strong negative influence). 197
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Fig. 2. Quantitative and qualitative targets with short- and long-term reward.
Fig. 3. Key steps involved in a digital expert system. Controller model combining physiological and behavioural responses. The control loop is determined by steps 3 to 5 and ‘delta’ between the set target (step 2) and the measured state (step 3).
pertinent. However, interfering or massaging data is a behavioural aspect of the user, so in the new methodology it does not actually matter if the user tweaks their data. This is a direct consequence of measuring combined physiological and behavioural effectiveness, and not physiological efficacy of the suggested intervention! The question of measurable feedback is interesting from the perspective of human psychology, since we know humans are naturally ‘wired’ to respond to immediate rewards rather than slow evolution. This may go some way to explaining the relatively small effect of public health advice in the face of instant gratification from all directions. The proposed methodology would allow, conceptually at least, the prospect of balancing long-term ‘low reward’ effects, such as weight loss, with shorter ‘instant gratification’ advice, such as glycaemic response, in order to improve adherence.
simplicity, weight can be used as an indicator of obesity. Given that height does not (usually) change in an adult, BMI could also be used – height would be a constraint for an adult, but a variable for a child. Following the previous procedure, we define a target weight. However, the increased complexity in obesity compared to the pre-diabetes example arises from two sources: Firstly, many more factors influence our obesity target variable than in our (simplified) diabetes example. This presents no particular conceptual or mathematical problem per se, although, the larger the modulator/constraint space becomes, the longer the learning process will take. Secondly, the response time of the measured target variable (weight) is much slower than the characteristic frequency of advice e.g. several per day, whereas the responses will only be meaningful over, perhaps, a month or longer. Once again, this is not a problem per se, but the search for effective strategies will progress more slowly than in the simpler case of diabetes. From a classical clinical research perspective, the question of possible user interference with the data is extremely
4. Roadblocks and possible solutions In this section, a few of the most important currently identified 198
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Trijp, 2007). Other parameters, such as efficiency, rapid access to trustworthy information, integration with additional information channels and backup solutions, in form of a real person, if the digital service does not provide the answer, might also be of importance. As already mentioned, reward, or at least positive feedback is a much stronger driver of engagement than avoiding negative consequences via advice. Self-assessed ‘soft’ variables such as ‘how tired do I feel’ are likely to be much more motivating than objective hard measures based on biomarkers e.g. BMI. An area that would warrant further exploration is the linking of fast, soft, variables, which motivate the user, together with slower long-term health related biomarkers. An alternative way to engage consumers might be through financial rewards, e.g. by health insurances providing a premium if users successfully adhere to the given advice, as such system can measure this. Any solution that has a chance of wide adoption would need to be capable of running on a wide range of platforms. This should not be difficult to achieve, since nowadays software is usually written in a platform independent manner. Measuring devices are also generally platform independent and communicate using non-proprietary protocols e.g. Bluetooth, ZigBee etc. Availability of devices at a suitably low price point is currently a barrier, at least in some areas of the world, although it is unlikely to remain so in the medium to long term.
challenges associated with realising the vision outlined in section three are highlighted. Where possible, we attempt to outline approaches and/ or solutions but, in some cases, where we lack insight, we have tried - at least – to state clearly the issue(s) that will need to be addressed. Note that, at the time of writing, the proposed approach is still conceptual and has therefore not yet been tested. Consequently, there are likely other important issues that remain to be discovered. 4.1. Determining scientifically sound risk factors The first, and perhaps the most obvious, need for implementation of a self-learning system, as outlined in section 3, are measurable targets linked to a risk factor. The results of any optimisation process are only as good as the target, since the objectives of the optimisation need to be defined in terms of appropriate target variables. So, what makes a good target variable? In our opinion, essentially two characteristics: Firstly, the target variable should be measurable in a simple, cheap and, ideally, non-invasive manner. Ideally, the time course should also be quantified on a timescale that is at least as rapid as the relaxation time of the feedback process under observation. For plasma glycaemia, this means a measurement frequency of minutes. The need for minimal invasiveness is clearly coupled to the required frequency of measurement, with high frequency measurements requiring a less invasive approach. Secondly, the target variable needs to have some consequential relationship with meaningful long-term health and wellness outcomes (L. Hood, Lovejoy, & Price, 2015). It is here that the current nutrition paradigm is struggling to deliver, and a new approach will likely be needed. There are an increasing number of advocates of longitudinal ‘n = 1’ clinical trials to address some of these issues (L. Hood & Flores, 2012; Leroy Hood & Friend, 2011). One could observe that self-learning systems of the kind we propose could also provide a useful research tool for these innovative studies. In the context of wellness and wellbeing, entirely subjective, selfassessment variables, e.g. tiredness, alertness, bloating, lethargy etc. are acceptable choices since the only requirement is that they should somehow convincingly link to the outcomes selected by the user. It is important to emphasise once again, that the objective here is not to scientifically understand the link between target and lifestyle/behaviours, merely to help the individual find the ‘path of least resistance’ to a desired outcome. Target variables will evolve with scientific insight and, potentially at least, pooling of data collected from personalized expert systems will help to drive this. It is notable that many clinically validated biomarkers with stronger links to disease consequences are ‘slow variables’. This is probably because point measures of fast variables in clinical trials would appear as noise and as such are not seen as useful from a cohort perspective. We should briefly mention that the consequences of confounding variables, and non-linear interactions, are no better, nor worse than existing approaches. This remains an unresolved issue.
4.3. Managing data overload and who to trust From the dawn of history, the brain has evolved to make rapid decisions, based on small amounts of data (heuristic reasoning). This has served us very well until relatively recently, when the trickle of information filtered via, for example, printed media, became a steady flow from first radio, then television and now the internet. With the advent of the digital revolution, the amount of information has become a raging torrent, with consequent demands on our time and reduction of attention spans. Despite this, people continue to believe that more data leads to better decisions, although studies in heuristics show that this is not the case (Gigerenzer & Gaissmaier, 2011). To reverse trends in NCDs, the proposed solution is improved filtering to reduce the amount of data seen by the client/user at the point of decision-making. The single nutrients as good or bad viewpoint e.g. low fat, low carb etc. arose from the need to communicate simple messages. Modern digital technology allows us to present complex messages in a simplified manner, opening the door to more nuanced and, ultimately, personalized advice. Although this solves the data overload problem without loss of control (i.e. the user still makes the final decision), it creates a problem of who or what to trust. Trust is not easily established and is rapidly lost. Traditionally, the trusted party has been human, typically a medical practitioner, a dietician, a financial advisor, NGO's, governments, etc., A recent study from Stewart-Knox and co-workers (Stewart-Knox et al., 2016) indicated, that government was more trusted than commerce to deliver and provide information on personalized nutrition. One important advantage of our approach is that it does not require any sharing of personal data in order to function. The personalization matrix is fully owned by the user and only stored on their personal device.
4.2. We need to understand how to engage the consumer in a sustainable way Currently, only a small fraction of the population is driven to manage their health (health disciples). Furthermore, health is rarely a consumer concern, as the feedback mechanisms of nutrition are much slower than hedonistic ones. Consumers will not engage with digital innovations if they do not fit painlessly into their lives in a reliable and sustainable (e.g. showing measurable progress) manner. Although complete engagement for the whole population remains a pipe dream, any system that manages to recruit even a small population segment is likely to have a positive impact on societal health and wellness. It is unrealistic to expect a single system to provide the solution for everyone (Gibney et al., 2016; Livingstone et al., 2016; Ronteltap & van
4.4. The need for technology standards and integration The volume of potential data is enormous. It has been estimated that personal lifestyle-based data sum up to 1100 terabytes over a lifespan; genetics and clinical data comprising 6.4 terabytes which is less than 1% of the total (IBM, 2016). It is unclear how these data will be catalogued, processed or accessed, but there are two potential approaches, currently: one would be to develop standards and metadata, such that keywords/phrases follow some kind of XML-like coding. This approach seems doomed to failure due to the near infinite growth of categories 199
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and all the problems with which we are familiar in our own filing systems, personal and professional. An alternative approach, which is rapidly maturing, would be the evolution of intelligent technology that no longer requires standards. Siri, Watson, Cortana, Alexa, Wolfram Alpha etc. show clear progress in this direction and would obviate the need for metadata. Regardless, there is a clear need to develop either a data standard or sufficient artificial intelligence to enable disparate systems to communicate and collaborate with each other.
dietary pattern. Journal of Education and Health Promotion, 3, 1. Boyd, A. (2010). Better health for individuals. Level, Vol. 2, Waterloo Road, North Ryde NSW 2113: McGraw-Hill Australia Pty Ltd82. Chen, C., Zhao, X. L., Li, Z. H., Zhu, Z. G., Qian, S. H., & Flewitt, A. J. (2017). Current and emerging technology for continuous glucose monitoring. Sensors, 17. Fern, E. B., Watzke, H., Barclay, D. V., Roulin, A., & Drewnowski, A. (2015). The nutrient balance concept: A new quality metric for composite meals and diets. PLoS One, 10, e0130491. Gibney, M., Gossens, J., & Walsh, M. (2016). Personalized Nutrition Paving the way to better population health. In M. Eggersdorfer,, K. Kraemer,, J. B. Cordaro,, J. Fanzo,, M. Gibney,, & E. Kennedy, (Eds.). Good nutrition: Perspectives for the 21st century (pp. 235–248). Basel, New York: Karger. Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482. Gray, A. (2015). Nutritional recommendations for individuals with diabetes. https://www. ncbi.nlm.nih.gov/books/NBK279012/?report=printable. Hood, L., & Flores, M. (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: Predictive, preventive, personalized and participatory. New Biotech, 29, 613–624. Hood, L., & Friend, S. H. (2011). Predictive, personalized, preventive, participatory (P4) cancer medicine. Nature Reviews Clinical Oncology, 8, 184–187. Hood, L., Lovejoy, J. C., & Price, N. D. (2015). Integrating big data and actionable health coaching to optimize wellness. BMC Medicine, 13, 4. IBM (2016). Precision health and wellness. IBM institute for business value (pp. 1–21). . Johnstone, A. M., Murison, S. D., Duncan, J. S., Rance, K. A., & Speakman, J. R. (2005). Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. American Journal of Clinical Nutrition, 82, 941–948. Kim, S. Y. (2013). Efficacy versus effectiveness. Korean J Fam Med, 34, 227. Ley, S. H., Hamdy, O., Mohan, V., & Hu, F. B. (2014). Prevention and management of type 2 diabetes: Dietary components and nutritional strategies. The Lancet, 383, 1999–2007. Livingstone, K. M., Celis-Morales, C., Navas-Carretero, S., San-Cristobal, R., Macready, A. L., Fallaize, R., et al. (2016). Effect of an internet-based, personalized nutrition randomized trial on dietary changes associated with the mediterranean diet: The Food4Me study. American Journal of Clinical Nutrition, 104, 288–297. McCarthy, M., Cluzel, E., Dressel, K., & Newton, R. (2013). Food and health research in Europe: Structures, gaps and futures. Food Policy, 39, 64–71. McGinnis, J. M., Williams-Russo, P., & Knickman, J. R. (2002). The case for more active policy attention to health promotion. Health Affairs, 21, 78–93. McGovern, L., Miller, G., & Hughes-Cromwick, P. (2014). Health policy brief - the relative contribution of multiple determinants to health outcomes. Project HOPE– The People-toPeople Health Foundation Inc. Ordovas, J. M., Ferguson, L. R., Tai, E. S., & Mathers, J. C. (2018). Personalised nutrition and health. BMJ, 361 bmj k2173. Ronteltap, A., & van Trijp, H. (2007). Consumer acceptance of personalised nutrition. Genes Nutr, 2, 85–87. Sonnenburg, E. D., & Sonnenburg, J. L. (2015). Nutrition: A personal forecast. Nature, 528, 484–486. Stewart-Knox, B. J., Markovina, J., Rankin, A., Bunting, B. P., Kuznesof, S., Fischer, A. R. H., et al. (2016). Making personalised nutrition the easy choice: Creating policies to break down the barriers and reap the benefits. Food Policy, 63, 134–144. USDA (2015). In USDA (Ed.). Dietary guidelines for Americans 2015–2020(8th ed.). . Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., et al. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163, 1079–1094.
5. Conclusions There is a growing awareness that the prevailing approach of population level nutritional advice cannot effectively address the needs of individuals, since even the best advice in the world is of no use if individuals are unable or unwilling to act upon it. We, therefore, propose a new paradigm that views the intervention/provision of advice and adherence of the user as inseparable – such that we measure only the effectiveness of behavioural advice on an individual health target. We have outlined how such a conceptual self-learning expert system might function in the context of (pre) type II diabetes and obesity. Based on the encouraging results from the pan-European Food4Me project, we are convinced that the new paradigm will considerably simplify the task of achieving sustainable health improvements for individuals by mapping their ‘path of least resistance’ in their daily context. We advocate that more public money be spent on the development of such selflearning expert systems, as these might offer a cost effective way to reduce the monetary and other burden related to NCDs. In closing, we should acknowledge that the system proposed here currently remains an untested concept. Although each of the elements alone have been tested to a greater or less extent in the past, to our knowledge, nobody has to date put the elements together to form a functioning system. We would encourage further work in this area to validate and evolve these concepts. Acknowledgement We would like to thank Tim Wooster, Annick Mercenier, Benjamin Le Reverend, and Prof. Erich Windhab for their valuable discussion that were the starting point for this document. We also thank Jörg Spieldenner, Christoph Hartmann, Imre Blank, Geert Meijer, Martin Leser, Jan Engmann and Sheldon Fernandes for their constructive input. References Asif, M. (2014). The prevention and control the type-2 diabetes by changing lifestyle and
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Commentary
Nutrition in the digital age - How digital tools can help to solve the personalized nutrition conundrum
T
M. Michel, A. Burbidge∗ Nestlé Research, Institute of Material Sciences, Department of Technology, Vers-Chez-Les-Blanc, CH-1000, Lausanne 26, Switzerland
A R T I C LE I N FO
A B S T R A C T
Keywords: Nutrition Personalization Life-style Non-communicable diseases Digital self-learning expert system Health
Background: It has become increasingly clear that the current population averaged nutrition paradigm is not able to address the growing non-communicable disease (NCD) epidemics. Current approaches fail primarily for two reasons: firstly, poor adherence to public dietary advice and, secondly, individual health responses are not well reflected by population averages, as generic public health advice lacks relevance for individuals, personally and medically. Scope and approach: Personalized ‘expert’ systems are, potentially, powerful weapons against NCDs. Existing systems are, however, handicapped by both difficulty in measuring dietary intake reliably and that of tying population level nutritional knowledge to stochastic individual responses. In order to address these shortcomings, we propose an approach that no longer distinguishes between behavioural and physiological responses. Key findings and conclusions: We outline how a conceptual self-learning expert system could implement this approach, based on multifactorial lifestyle interventions, and give specific examples in the contexts of diabetes and obesity. Combining behaviour and physiological responses into a single entity removes the requirement to measure food intake, enabling users to map their individualised ‘path of least resistance’ to specific health outcomes. This new approach could be provided at minimal cost by leveraging users existing mobile devices, e.g. smart phones, watches, fitbits etc. The novelty in this concept is that the methodology purposely does not attempt to understand the complexity of the underlying physiological, metabolic and psychological responses. Despite requiring scientifically validated biomarkers, understanding the discrete influences of each of these factors is not required to drive improved individual-level outcomes.
1. What does not work in today’s nutrition paradigm? The definition of essential nutrients and nutrient requirements scientifically underpins nutrient-based dietary advice. There are, however, obvious limitations in this reductionist approach, not least that people consume foods and not just individual nutrients. A large variety of ingredients and food products are combined in varying amounts to form diets and it is diets that are healthy or otherwise, not individual foods or ingredients. Thus, it is difficult, if not impossible, to determine the precise formulation of an individual product that can, when combined with other foods, provide nutritionally adequate diets under all circumstances. Dietary advice based on the food pyramid (USDA, 2015) have so far failed to prevent the growing rates of obesity and other nutrition-related chronic diseases (Gibney, Gossens, & Walsh, 2016). There is nothing wrong per-se with reformulation of food products, but this alone will not solve the problems associated with unhealthy dietary choices and lifestyles. Furthermore, individuals are far from
∗
standardized. The ability of the human body to absorb and utilize nutrients depends on various factors such as age, health, genetic make-up, personal nutritional status (e.g., those low in iron will usually absorb it more efficiently from diet – unless of course malabsorption is their base issue) and activity patterns. Differences in caloric needs of individuals are far more complex than just a function of BMI and are also strongly influenced by lifestyle (Johnstone, Murison, Duncan, Rance, & Speakman, 2005). Recent literature has demonstrated that the responses of individuals are often very different for identical nutritional stimuli, as demonstrated by, for example, postprandial glycaemia (Sonnenburg & Sonnenburg, 2015; Zeevi et al., 2015). Consequently, there is a need to tailor advice to the particular circumstances of an individual. Indeed the recently completed pan-European Food4Me project demonstrated that personalized nutrition can be feasible and effective and that the delivery of personalized advice is more effective in changing dietary intakes than general healthy eating messages (Gibney et al., 2016; Livingstone et al., 2016; Ronteltap & van Trijp,
Corresponding author. E-mail address: [email protected] (A. Burbidge).
https://doi.org/10.1016/j.tifs.2019.02.018 Received 18 July 2018; Received in revised form 8 October 2018; Accepted 6 February 2019 Available online 08 February 2019 0924-2244/ © 2019 Elsevier Ltd. All rights reserved.
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financial resources go into medical care while almost nothing is spent on improving behavioural patterns. This is unlikely to change in the short-term focused political environment. Table 1 lists some of the more obvious health influence factors (Boyd 2010). It is notable that the selection spans a range from highly modifiable through to non-modifiable health influence factors. The individual can adjust some of these, such as smoking, diet and physical activity, whereas others such as age or heredity are completely out of their control. The health influence factors can raise (e.g. a high caloric diet in combination with little or no physical activity) or reduce (e.g. eating a fibre rich diet) the risk level of an individual. Clinical testing of these health influence factors measures the effect of an imposed treatment on a particular health outcome, usually a validated biomarker such as serum glucose level, blood pressure etc. Generally, it is believed that a deeper scientific understanding of the fundamental biological mechanisms underlying different health determinants will suggest pathways to a solution. Although perfectly valid, this approach fails to take account of adherence of the free-living individual to the recommended treatment. From the perspective of outcomes, even the best dietary advice in the world is indistinguishable from the worst when the individual does not or cannot adhere to the advice due to specific circumstances, e.g. place of residence, access to healthy foods, employment conditions etc. This is the root cause of ineffectiveness in most public health campaigns. In order to address this problem, we propose a shift from measuring efficacy towards measuring the effectiveness of dietary advice on particular health target(s). The key difference is that we define efficiency as the composite effect of the treatment and adherence, rather than the effect of treatment alone. As we shall see in the following sections, this greatly simplifies our intervention strategy and opens the door to further personalization. This terminology finds a parallel in clinical trial nomenclature, where there are significant differences between efficacy and effectiveness trials. Randomized clinical trials of drugs or procedures are most often efficacy studies that are designed to show whether the drug or procedure produces the desired clinical outcome under optimal conditions. In contrast, effectiveness trials focus on whether a given drug or procedure works under usual circumstances (Kim, 2013). In clinical trials parlance, our proposed approach is a form of adaptive, continuous, ‘personalized (n = 1) effectiveness trial’.
2007). In summary, poor nutrition is a consequence of an inappropriate quantity and quality of food in the diet. The problems of diet-related NCDs cannot be addressed effectively using traditional, population level nutritional advice. As McCarthy mentions, food and health research needs to move from ‘healthy food’, which concentrates on food as a product, to research for ‘healthy eating’, which is concerned with appropriate overall food intake adapted to an individual's situation (McCarthy, Cluzel, Dressel, & Newton, 2013). In the remainder of this article, we will argue for a paradigm shift, away from the current research focus of measuring the effect of a prescribed, but often not followed, diet on population-averaged cohorts, towards measuring the efficiency of dietary and lifestyle advice for an individual. We will sketch out how recent advances in digital technologies can both enable and accelerate this change. Our objective will be to optimise the effectiveness of personalized advice; not to better understand the underlying causes of observed response. This is a crucial simplification that, as we will argue in the following sections, obviates the need to measure dietary intake. Removing this need is an important step forward, since one of the most difficult parts of developing diet management systems is in quantifying intake reliably and simply.
2. Integrating nutrition in the context of an individuals’ lifestyle Clearly, nutrition is not the only factor that determines the health and wellness of an individual. Nevertheless, it is a significant and essential part of a complex web of inter-related ‘health influence factors’ (see Table 1) that include individual behaviours, environmental factors, and heredity. The relative impact of each of these on premature death in the US is estimated as follows (McGinnis, Williams-Russo, & Knickman, 2002; McGovern, Miller, & Hughes-Cromwick, 2014):
• 30% genetic predispositions • 15% social circumstances • 5% environmental exposures • 40% behavioural patterns • 10% shortfalls in medical care These numbers demonstrate that behaviours are responsible for almost half of all premature deaths. Even this is an underestimation of the overall cost to society since no account is taken of co-morbidities (e.g. diabetes associated with obesity, muscular-skeletal issues associated with excess weight and obesity, etc.) that affect an even larger population group without necessarily killing them. Over the medium to longterm, these health-influencing factors both modulate behaviours and strongly influence health. Strictly speaking, health influence factors are stochastic variables modulating NCD risk, although the reality is slightly more complex since individuals that behave in a very risky fashion can ‘get lucky’ and vice-versa. It is notable that today, most
3. From generic dietary advice towards personal eating efficiency This section lays out a vision of how digital technologies can help individuals towards better eating habits. There are a number of potential roadblocks to implementation such as identifying scientifically sound target variables, engagement of the consumer in a sustainable way and managing data overload and trust. Discussion of these issues will be postponed until section four.
Table 1 Modifiable and non-modifiable ‘health influence factors’. Adapted from (Boyd, 2010).
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3.1. What does personalized nutrition mean?
the following steps (see Fig. 3):
As outlined by Ordovas and co-workers (Ordovas, Ferguson, Tai, & Mathers, 2018), personalized nutrition pursues the idea that individualising nutritional advice, products, or services will be more effective than approaches based on generic advice. There are two aspects that need to be addressed: 1) physiological responses to foods/nutrients, and, 2) individual behavioural patterns including, preferences, barriers, and motivations. The recent Food4me program provides evidence that internet delivery of personalized nutrition is more effective than traditional generic dietary advice. Furthermore, the inclusion of genetic data did not appear to add additional benefits (Livingstone et al., 2016). These results encourage further personalized intervention. Up until the time of writing, most research has tended to focus on either the medical or the behavioural aspects of the problem in isolation. In the remainder of this paper, we will argue that there are potential benefits to combining these two approaches using digital devices. Mobile devices are now ubiquitous in the developed world and are rapidly becoming available everywhere. Many cheap portable sensors that interface with these devices are also emerging. Taken together with data connectivity, the internet of things and validated nutritional databases they provide an excellent foundation for the development of personalized self-learning systems. An important advantage of such digital expert systems is their capacity to take account a multitude of personal data channels, such as dietary habits including personal preferences, health status, physical activity, sleep patterns, personal value landscape, etc. As Gibney and co-workers mention (Gibney et al., 2016), analysing and combining data from many different channels is likely to add the most value for managing the health and well-being of individuals. In our new approach, the basic requirement for a digital expert system is to measure the effectiveness of dietary or lifestyle advice, e.g. how the device owners plasma glucose level changes as a function of an advice (e.g. try to avoid sugar rich beverages). From a concrete perspective, this requires that both the sensitivity (gain) and directionality (positive or negative) of advice (e.g. example for diabetes in Table 3) on chosen target(s) (Fig. 2) be quantified. Mathematically speaking, the system attempts to quantify the mapping between the advice (input variables) and outcomes (response variables). In a linear system, the advice and the outcomes are both vectors, and the mapping is a personalized transfer function matrix (see Fig. 1). In a self-learning system, the personalized mapping evolves over time, based on measured responses to advice. For our purposes, we need fast variables for two reasons – (1) learning rate of the system itself for the individual is directly proportional to how long it takes to ‘explore the space’ (2) maintaining the user's attention and providing positive reinforcement feedback that the intervention is having some positive effect as illustrated in Fig. 2. How could/would such a system operate? The concept is based on a classical feedback controller model, as used for over a century in a broad range of applications, and involves
1) identify what to improve (see Fig. 2). 2) set target e.g. validated biomarker (see Table 2) and initialize personalization matrix using state of the art population based nutrition knowledge 3) measure - ideally non-invasive 4) determine deviation from target (delta) 5) provide advice e.g. specific products to avoid/to eat, meal & training plan etc. (see Table 3) 6) learn about progress towards set target(s). Update ‘Personalization’ matrix. 7) optional: individual gives input on determinants/advice used - if available will speed up learning process Progress around this ‘closed loop’ (step 5 loops back to step 3 and repeats) over a period of time will evolve a specific set of mappings between advice and the defined target. In other words, the system learns what is easy for the individual to do in order to achieve set target (s) (finding a personal ‘path of least resistance’). This set of mappings will be unique to the individual and encapsulate their personal map of constraints and effective modulators. Since this map (personalization matrix) only needs to be stored on the user's device, they own the data but could also opt to share the data on a voluntary basis. Data obtained from a cohort of users would be of value to researchers and companies to stratify sub-groups of consumers. Such stratification would be particularly useful for improving the system's speed of learning since there are likely to be common features between certain sub-groups. Starting with the best available nutritional knowledge (Asif, 2014; Fern, Watzke, Barclay, Roulin, & Drewnowski, 2015; Ley, Hamdy, Mohan, & Hu, 2014) is likely to be very important, since we know that people have limited patience if they do not see a benefit. Furthermore, gaps in the market (e.g. breakfast cereals with low sugar content), could be identified in this way, which could stimulate food producers to develop better product offerings for particular consumer groups. Choosing an appropriate target and associating it to a suitable control variable is a prerequisite to implement the above scheme. Fig. 2 gives some examples of target choices, which fall broadly into two categories: (i) validated biomarkers, (ii) subjective self-assessments. The first category are generally measurable in an objective quantitative fashion and relate to specific medical conditions. The second group covers the wider area of ‘wellness’ and ‘wellbeing’, so in this case are directly perceived by the user. This difference in the nature of target variables has a very important influence on the psychological compliance aspect of device/advice taking. In the helicopter view, the medical biomarkers are long-term preventative, in contrast to the selfassessments, which are short-term ‘rewards’. Balancing ‘reward’ and ‘prevention’ is an emerging opportunity, which we will discuss further in section 4. 3.2. Example risk factor diabetes In the current context, diabetes can be viewed as an inability to manage plasma glucose concentration in response to nutrient intake. Of particular interest are pre-(type II) diabetic people, who show the first indications of inadequate plasma glucose response (i.e. poor glucose tolerance), which can still be controlled through nutritional interventions. From the perspective of the control system, the first step is to identify a suitable control variable (Table 2 lists some options). An effective control variable needs to be both coupled to the desired outcome, and exhibit a relatively rapid response to nutrient intake (i.e. biological system response time is comparable to the timescale of eating events). From this perspective, the ideal control variable would appear to be plasma glycaemia itself. However, diagnosed type II diabetics are likely to be taking medication to control plasma glycaemia in which case the glycaemia itself would probably not be a good indicator. There
Table 2 Biomarkers for individuals with diabetes adapted from (Gray, 2015). DIABETES IndicatorS
Target range
Speed of response
A1C Blood Pressure LDL Cholesterol Triglycerides HDL Cholesterol
< 7% < 140/80 mm Hg < 100mg/dL < 150mg/dL > 40mg/dL (Men) > 50mg/dL (Women) 20–25 < 7.8mmol/L
Slow (months) Slow (with fast fluctuations) Slow Slow (with fast fluctuations) Slow Slow Slow Fast (Minutes)
Body Mass Index Plasma Glycemia
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Table 3 Advice for diabetes prevention adapted from: (Asif, 2014; Ley et al., 2014), https://www.hsph.harvard.edu/nutritionsource/disease-prevention/diabetesprevention/preventing-diabetes-full-story/#weight-control. DIET ADVICE
OTHER ADVICE
Focus on plant foods to increase fibers: Choose whole grains and whole grain products over highly • processed carbohydrates. Cut back on refined carbs and sugary drinks. White bread, white rice, white pasta and potatoes cause • quick increases in blood sugar, as do sugary soft drinks, fruit punch, and fruit juice. Over time, eating
• • • •
lots of these refined carbohydrates and sugar may increase your risk of type 2 diabetes. To lower your risk, switch to whole grains and skip the sugar, especially the sugary drinks, and choose water, coffee, or tea instead. Choose healthy fats and proteins, and skip the red and processed meat. A diet rich in mono and polyunsaturated fats can help lower your risk of diabetes and heart disease. Canola oil and olive oil are great choices, as are the fats in avocados, nuts, and seeds. Limit red meat and avoid processed meat; choose nuts, whole grains, poultry, or fish instead. Substituting low–glycemic load foods for higher–glycemic load foods may modestly improve glycemic control. Alcohol now and then may help
briskly for a half hour every day reduces the risk of • Walking developing type 2 diabetes by 30 percent. (18, 19) • Reduce watching TV
• Use stairs instead of elevators instead of driving • Walk • Go to gym • Stop smoking
Following the advice, individuals are free to act as they wish. This is somewhat counterintuitive in establishing the effectiveness of a strategy, but there is no need to measure whether or not the user takes the advice! This is an extremely important point. It is valid only because, in the new approach, we choose not to distinguish between the specific effect of a particular intervention and the adherence of the user. We only need to know how effective a particular advice was in achieving the goal; not why it did or did not work. Taking the example of glycaemia, either eating food with a low GI or a course of aromatherapy are likely to be very different from an efficacy perspective. However, if the individual does not follow the low GI advice then the effectiveness of these two recommendations are indistinguishable. Finally, the response is analysed – this is the crux of establishing whether a suggested strategy is effective or not for the individual user. Effectiveness is defined as progress of the target variable in the desired direction – i.e. did the advice influence the ‘delta’ between the set target and the measured state. There are three possible outcomes: (1) delta was reduced, in which case the advice is defined as a positive modulator with a quantifiable effectiveness, (2) delta increased, thus the advice is defined as a negative modulator (antagonist) to be avoided in this context, (3) delta was unchanged, in which case the advice is actually a constraint (i.e. a modulator with a ‘gain’ of zero). In this manner, the ‘personalization matrix’ is evolved over time (cycles around the closed loop controller) from the generic starting point to a bespoke version. This (pre) type II diabetes example is quite simple because we have a very clear, single target variable and a very rapid feedback mechanism. One of the big advantages of this approach is that there is no need to know the exact underlying physiological mechanisms for it to work. Other targets are much more complex, but the general approach remains valid. To demonstrate this, we will now discuss the more complex example of obesity reduction.
are also regulatory and potential ethical barriers to the use of an invalidated device in a medical context. For these reasons, we will focus on pre-diabetics for the remainder of this section and select plasma glycaemia as our primary response variable. Obviously slow variables such as BMI can still be reported, and improvements in these are also expected and can be monitored by the system in such a manner that they can be secondary optimisation targets. Working from Fig. 3, we have the following sequence of events: The risk is pre type II diabetes and our target is improved control of plasma glucose response. The measured variable is plasma glucose concentration itself, and there is technology available that provides a non-invasive, time continuous readout of this (e.g. FreeStyle Libre Flash glucose measuring system from Abbott) (Chen et al., 2017). The target value is that basal glucose concentration, which should be maintained below 7 mMol/L and extreme variations should be avoided, thus – from a control perspective – the aim is to minimise the dynamic plasma glucose changes. The next step is to establish the possible influence factors and constraints. It is important to understand the conceptual difference between a ‘modulator’ and a ‘constraint’, as this is key to personalizing the system. Clearly, we will have some ‘hard constraints’ (e.g. genetics), but almost everything else will appear on a spectrum between a pure ‘constraint’ and a ‘perfect modulator’ (Table 1). Individualisation of the system requires learning whether something is a constraint or a modulator for a particular individual. Some of these data are easily obtained (e.g. perhaps an individual has an allergy to a particular protein or is lactose intolerant), but many factors must be learned by the system. Existing population level nutritional knowledge is a good starting point (e.g. Table 3); in the current context, this would likely include minimising the consumption of particular foodstuffs with a high glycaemic index (GI) and/or diets with a high glycaemic load (GL). Once the system establishes the current state of constraint and has identified the most likely ‘modulators’, advice will be given to the user. In this case, typical advice might be to avoid sugary beverages (high GI and GL). Since the aim is to be self-learning, advice will differ over time in order to explore the effectiveness of various modulators and the rigidity of constraints. Essentially, the modulator-constraint space for the individual user is being explored.
3.3. Example obesity Obesity is a much looser concept than diabetes, so a more specifically quantifiable goal is required. A subtle point worth mentioning here is that the measure could also be completely subjective (e.g. how tired do I feel) for the argument to apply equally well. For the sake of Fig. 1. Schematic representation of a linear response system. The aim of the self-learning system is to systematically vary the ‘Advice Vector’ {A whilst measuring the ‘Outcome Vector’ {O} such that the ‘Personalization Matrix’ [P] is adapted to the user (++ strong positive influence, + moderate positive influence, 0 no influence, - moderate negative influence, – strong negative influence). 197
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Fig. 2. Quantitative and qualitative targets with short- and long-term reward.
Fig. 3. Key steps involved in a digital expert system. Controller model combining physiological and behavioural responses. The control loop is determined by steps 3 to 5 and ‘delta’ between the set target (step 2) and the measured state (step 3).
pertinent. However, interfering or massaging data is a behavioural aspect of the user, so in the new methodology it does not actually matter if the user tweaks their data. This is a direct consequence of measuring combined physiological and behavioural effectiveness, and not physiological efficacy of the suggested intervention! The question of measurable feedback is interesting from the perspective of human psychology, since we know humans are naturally ‘wired’ to respond to immediate rewards rather than slow evolution. This may go some way to explaining the relatively small effect of public health advice in the face of instant gratification from all directions. The proposed methodology would allow, conceptually at least, the prospect of balancing long-term ‘low reward’ effects, such as weight loss, with shorter ‘instant gratification’ advice, such as glycaemic response, in order to improve adherence.
simplicity, weight can be used as an indicator of obesity. Given that height does not (usually) change in an adult, BMI could also be used – height would be a constraint for an adult, but a variable for a child. Following the previous procedure, we define a target weight. However, the increased complexity in obesity compared to the pre-diabetes example arises from two sources: Firstly, many more factors influence our obesity target variable than in our (simplified) diabetes example. This presents no particular conceptual or mathematical problem per se, although, the larger the modulator/constraint space becomes, the longer the learning process will take. Secondly, the response time of the measured target variable (weight) is much slower than the characteristic frequency of advice e.g. several per day, whereas the responses will only be meaningful over, perhaps, a month or longer. Once again, this is not a problem per se, but the search for effective strategies will progress more slowly than in the simpler case of diabetes. From a classical clinical research perspective, the question of possible user interference with the data is extremely
4. Roadblocks and possible solutions In this section, a few of the most important currently identified 198
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Trijp, 2007). Other parameters, such as efficiency, rapid access to trustworthy information, integration with additional information channels and backup solutions, in form of a real person, if the digital service does not provide the answer, might also be of importance. As already mentioned, reward, or at least positive feedback is a much stronger driver of engagement than avoiding negative consequences via advice. Self-assessed ‘soft’ variables such as ‘how tired do I feel’ are likely to be much more motivating than objective hard measures based on biomarkers e.g. BMI. An area that would warrant further exploration is the linking of fast, soft, variables, which motivate the user, together with slower long-term health related biomarkers. An alternative way to engage consumers might be through financial rewards, e.g. by health insurances providing a premium if users successfully adhere to the given advice, as such system can measure this. Any solution that has a chance of wide adoption would need to be capable of running on a wide range of platforms. This should not be difficult to achieve, since nowadays software is usually written in a platform independent manner. Measuring devices are also generally platform independent and communicate using non-proprietary protocols e.g. Bluetooth, ZigBee etc. Availability of devices at a suitably low price point is currently a barrier, at least in some areas of the world, although it is unlikely to remain so in the medium to long term.
challenges associated with realising the vision outlined in section three are highlighted. Where possible, we attempt to outline approaches and/ or solutions but, in some cases, where we lack insight, we have tried - at least – to state clearly the issue(s) that will need to be addressed. Note that, at the time of writing, the proposed approach is still conceptual and has therefore not yet been tested. Consequently, there are likely other important issues that remain to be discovered. 4.1. Determining scientifically sound risk factors The first, and perhaps the most obvious, need for implementation of a self-learning system, as outlined in section 3, are measurable targets linked to a risk factor. The results of any optimisation process are only as good as the target, since the objectives of the optimisation need to be defined in terms of appropriate target variables. So, what makes a good target variable? In our opinion, essentially two characteristics: Firstly, the target variable should be measurable in a simple, cheap and, ideally, non-invasive manner. Ideally, the time course should also be quantified on a timescale that is at least as rapid as the relaxation time of the feedback process under observation. For plasma glycaemia, this means a measurement frequency of minutes. The need for minimal invasiveness is clearly coupled to the required frequency of measurement, with high frequency measurements requiring a less invasive approach. Secondly, the target variable needs to have some consequential relationship with meaningful long-term health and wellness outcomes (L. Hood, Lovejoy, & Price, 2015). It is here that the current nutrition paradigm is struggling to deliver, and a new approach will likely be needed. There are an increasing number of advocates of longitudinal ‘n = 1’ clinical trials to address some of these issues (L. Hood & Flores, 2012; Leroy Hood & Friend, 2011). One could observe that self-learning systems of the kind we propose could also provide a useful research tool for these innovative studies. In the context of wellness and wellbeing, entirely subjective, selfassessment variables, e.g. tiredness, alertness, bloating, lethargy etc. are acceptable choices since the only requirement is that they should somehow convincingly link to the outcomes selected by the user. It is important to emphasise once again, that the objective here is not to scientifically understand the link between target and lifestyle/behaviours, merely to help the individual find the ‘path of least resistance’ to a desired outcome. Target variables will evolve with scientific insight and, potentially at least, pooling of data collected from personalized expert systems will help to drive this. It is notable that many clinically validated biomarkers with stronger links to disease consequences are ‘slow variables’. This is probably because point measures of fast variables in clinical trials would appear as noise and as such are not seen as useful from a cohort perspective. We should briefly mention that the consequences of confounding variables, and non-linear interactions, are no better, nor worse than existing approaches. This remains an unresolved issue.
4.3. Managing data overload and who to trust From the dawn of history, the brain has evolved to make rapid decisions, based on small amounts of data (heuristic reasoning). This has served us very well until relatively recently, when the trickle of information filtered via, for example, printed media, became a steady flow from first radio, then television and now the internet. With the advent of the digital revolution, the amount of information has become a raging torrent, with consequent demands on our time and reduction of attention spans. Despite this, people continue to believe that more data leads to better decisions, although studies in heuristics show that this is not the case (Gigerenzer & Gaissmaier, 2011). To reverse trends in NCDs, the proposed solution is improved filtering to reduce the amount of data seen by the client/user at the point of decision-making. The single nutrients as good or bad viewpoint e.g. low fat, low carb etc. arose from the need to communicate simple messages. Modern digital technology allows us to present complex messages in a simplified manner, opening the door to more nuanced and, ultimately, personalized advice. Although this solves the data overload problem without loss of control (i.e. the user still makes the final decision), it creates a problem of who or what to trust. Trust is not easily established and is rapidly lost. Traditionally, the trusted party has been human, typically a medical practitioner, a dietician, a financial advisor, NGO's, governments, etc., A recent study from Stewart-Knox and co-workers (Stewart-Knox et al., 2016) indicated, that government was more trusted than commerce to deliver and provide information on personalized nutrition. One important advantage of our approach is that it does not require any sharing of personal data in order to function. The personalization matrix is fully owned by the user and only stored on their personal device.
4.2. We need to understand how to engage the consumer in a sustainable way Currently, only a small fraction of the population is driven to manage their health (health disciples). Furthermore, health is rarely a consumer concern, as the feedback mechanisms of nutrition are much slower than hedonistic ones. Consumers will not engage with digital innovations if they do not fit painlessly into their lives in a reliable and sustainable (e.g. showing measurable progress) manner. Although complete engagement for the whole population remains a pipe dream, any system that manages to recruit even a small population segment is likely to have a positive impact on societal health and wellness. It is unrealistic to expect a single system to provide the solution for everyone (Gibney et al., 2016; Livingstone et al., 2016; Ronteltap & van
4.4. The need for technology standards and integration The volume of potential data is enormous. It has been estimated that personal lifestyle-based data sum up to 1100 terabytes over a lifespan; genetics and clinical data comprising 6.4 terabytes which is less than 1% of the total (IBM, 2016). It is unclear how these data will be catalogued, processed or accessed, but there are two potential approaches, currently: one would be to develop standards and metadata, such that keywords/phrases follow some kind of XML-like coding. This approach seems doomed to failure due to the near infinite growth of categories 199
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and all the problems with which we are familiar in our own filing systems, personal and professional. An alternative approach, which is rapidly maturing, would be the evolution of intelligent technology that no longer requires standards. Siri, Watson, Cortana, Alexa, Wolfram Alpha etc. show clear progress in this direction and would obviate the need for metadata. Regardless, there is a clear need to develop either a data standard or sufficient artificial intelligence to enable disparate systems to communicate and collaborate with each other.
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5. Conclusions There is a growing awareness that the prevailing approach of population level nutritional advice cannot effectively address the needs of individuals, since even the best advice in the world is of no use if individuals are unable or unwilling to act upon it. We, therefore, propose a new paradigm that views the intervention/provision of advice and adherence of the user as inseparable – such that we measure only the effectiveness of behavioural advice on an individual health target. We have outlined how such a conceptual self-learning expert system might function in the context of (pre) type II diabetes and obesity. Based on the encouraging results from the pan-European Food4Me project, we are convinced that the new paradigm will considerably simplify the task of achieving sustainable health improvements for individuals by mapping their ‘path of least resistance’ in their daily context. We advocate that more public money be spent on the development of such selflearning expert systems, as these might offer a cost effective way to reduce the monetary and other burden related to NCDs. In closing, we should acknowledge that the system proposed here currently remains an untested concept. Although each of the elements alone have been tested to a greater or less extent in the past, to our knowledge, nobody has to date put the elements together to form a functioning system. We would encourage further work in this area to validate and evolve these concepts. Acknowledgement We would like to thank Tim Wooster, Annick Mercenier, Benjamin Le Reverend, and Prof. Erich Windhab for their valuable discussion that were the starting point for this document. We also thank Jörg Spieldenner, Christoph Hartmann, Imre Blank, Geert Meijer, Martin Leser, Jan Engmann and Sheldon Fernandes for their constructive input. References Asif, M. (2014). The prevention and control the type-2 diabetes by changing lifestyle and
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