Tailoring digital health interventions: Different strategies, different effects

By Emma Norris, on 24 September 2019

By Maria Altendorf – University of Amsterdam, Netherlands

In September 2019, the annual conference of the European Health Psychology Society (EHPS) took place in beautiful Dubrovnik at the Adriatic coast in Croatia. During this conference, the first symposium from the EHPS Special Interest Group Digital Health & Computer-Tailoring (@EHPSDigiHealth) entitled “Tailoring digital health interventions: Different strategies, different effects” was organized and chaired by Eline Smit. The symposium was dedicated to introducing the audience to a wide variety of novel approaches to tailoring in digital health, as well as their different effects on health behavior and health behavior change. The tailoring strategies covered by the presentations in the symposium were: message frame tailoring, mode tailoring, customization, as well as data-driven tailoring. Moreover, a systematic review of tailored digital interventions for weight loss, especially focusing on the differential effects of different tailoring types was presented and discussed.

Image courtesy of @EHPSDigiHealth

From message framing towards message frame tailoring in online computer-tailored smoking cessation communication

The first presenter was Maria Altendorf. She showcased an experimental research on the effects of autonomy-supportively framed smoking cessation messages in an online computer-tailored health communication intervention, compared with messages that used a controlling frame. Earlier research showed that people may be more motivated when they perceive support for their autonomy. Therefore, autonomy-supportive message frames seem to be a promising manner to enhance people’s motivation and eventually change their behaviour, such as to move them towards quitting smoking. In this study, autonomy-supportive messages comprised the use of suggestive language and words as “could” and “would”, as well as offering choice. Controlling messages in contrast made use of commands and words as “must” and no provision of choice. Result showed that participants’ motivation to quit smoking was not significantly enhanced through the autonomy-supportively framed messages. However, on average, participants had a positive opinion about the smoking cessation messages and also perceived high levels of autonomy-support – regardless of the condition they were randomized into. Furthermore, participants reported a need for autonomy, which means that the people participating in our study generally preferred to make their own choices regarding health decisions.

The findings result in many more questions, such as:

• Is it the internet environment that leads to these relatively high levels of perceived autonomy-support?
• Do people with a higher need for autonomy generally perceive higher levels of autonomy-support?

These are some questions that should – and will – be investigated further. More information (in Dutch) about the research project this study was part of, can be found here.

Text, images, video? Tailoring the modality of presentation in online health information for older patients.

Next, Minh Hao Nguyen pointed towards the potential benefit of providing patients with preparatory online information, which can improve patients’ processing of medical information. She presented a randomized controlled trial among 232 younger and older Dutch patients with cancer which aimed to test the effectiveness of a tailored website (vs. non-tailored website) on pre- and post-hospital visit patient outcomes. Different from traditional tailoring research focusing mainly on content, this educational website tailored the information to patients preferences for the mode of presentation (i.e., by self-selecting text, images and/or videos). The findings showed that the mode-tailored website increased website satisfaction and decreased anxiety in younger patients (<65 years). These patterns were not found among older patients (≥65 years). Both the tailored and non-tailored websites were surprisingly well used by patients (on average 34 minutes). Additional analyses showed that higher website involvement and greater website satisfaction explained the level of knowledge patients acquired from the website, before going to the hospital. Higher knowledge, in turn, predicted patients’ recall of information from the consultations with their healthcare provider. To conclude, offering online health information may benefit patients’ information processing. Moreover, offering this information in a tailored manner can be optimal for some patients. Thus, mode tailoring is a promising strategy to optimize the effectiveness of online health information for cancer patients.

Based on these results, Minh Hao recommended future research to further disentangle the active ingredients of tailored websites for different patient populations:

• How, when, and for whom exactly does mode tailoring have an added value?

Does combining mode tailoring with other strategies, such as content tailoring, lead to greater effects that exceed the sum of their individual effects?

More information about the research project can be found here, and the full dissertation with other related studies can be found here.

Customizable digital environments: can customization in mobile apps support physical activity?

Research showed that health behaviour change can be promoted by allowing users to customize their mobile health apps. Therefore, Nadine Bol investigated in an experiment the question of why and for whom the customization of mobile health apps is most effective. It appeared that participants with a higher need for autonomy increased their intention to engage in physical activity after engaging with a customizable health app, whereas participants with a lower need for autonomy did not. This finding suggests that differences in the need for autonomy should be considered to optimise the impact of mobile health apps.

Based on these results, one could raise the question of how to deal with participants with a lower need for autonomy:

• Is customisation even an option for people with a lower need for autonomy or should they be guided by clear-cut expert advice, as suggested elsewhere?

A systematic review of tailored eHealth interventions for weight loss: a focus on tailoring methodology

Then, Kathleen Ryan provided insights into the current evidence for tailored eHealth weight-loss interventions. She presented results of a systematic review about how tailoring was implemented and whether these tailored approaches were effective in producing weight loss compared with generic or inactive control. Tailoring was carried out in a number of ways, such as based on anthropometric data, health-related behaviours (e.g. dietary intake, physical activity), and participant location. Also, the data for tailoring was acquired from a range of manners, for instance from online questionnaire administration, dynamic gathering of data from web-based diaries, or mobile applications. Overall, tailored interventions were more effective in supporting weight loss than generic or waitlist control groups but effect sizes were very small to moderate, with evidence for fluctuations in effect sizes and differences of effect between tailoring and non-tailoring interventions, and between tailoring types, over time.

Based on these findings, Kathleen developed a model of tailoring depth, which categorized the many different approaches to tailoring used within weight loss interventions and provided a suite of examples of tailoring methods. This model highlighted the concept of ‘tailoring depth’ or the degree to which an intervention is personalized, contrary to viewing interventions as either ‘tailored’ or not, which is unhelpful for determining the effectiveness of different tailoring strategies. Moreover, one could ask:

• What are the mechanisms at play among the fundamental differences behind different tailoring approaches?

Quality assessment of artificial intelligence to tailor a digital health intervention for smoking cessation.

The last presentation was given by Santiago Hors-Fraile, who could not attend the symposium in-person. Via a virtual presentation – matching the digital moto of the event perfectly – he highlighted the potential of health recommender systems based on Artificial Intelligence (AI). In his talk, he showed how health recommendations can be provided that learn over time based on AI, instead of using traditional static decision rules as it is common practice in most computer-tailored interventions nowadays. In his study, he validated this innovative data-driven tailoring approach to assess the quality of an AI algorithm which tailored motivational messages for people willing to quit smoking. Preliminary results show that machine learning-based health recommender systems can be used to provide relevant messages to support smoking cessation patients. Moreover, the used algorithm learnt from patients’ preferences to recommend and tailor health messages and participants stated high levels of satisfaction with the system.

In the general discussion following Santiago’s talk, same questions were posed:

• Will humans (i.e. nurses or coaches) eventually be replaced through computer-tailoring?
• And should these web, mobile app and data-driven approaches be considered the “holy grail” for the future of behaviour change? – Should they? Or do we need empathy and a human being to also motivate us, or even support us in certain situations?
• What would happen to those for whom computerized tailoring is not effective? How could we reach those people?

Bio

Maria Altendorf is a PhD-student at the department of Communication Science, University of Amsterdam, the Netherlands. In her PhD research she investigates the effects of message frame tailoring in online health communication for individuals who intend to change their unhealthy lifestyle. Moreover, Maria is interested in the assessment and the implementation of health behaviour change interventions on a broader scale. She is supervised by Prof. Julia van Weert, Dr. Ciska Hoving, and Dr. Eline Smit and currently finalizing her dissertation.

Her research is supported by the Dutch Cancer Society (grant number: KWF 2015-7913).

You can find Maria here:

Linked in: Maria Altendorf (https://www.linkedin.com/in/maria-altendorf-377b2495/)

Research gate: https://www.researchgate.net/profile/Maria_Altendorf

Twitter: @maria_altendorf

Preparing for the ‘Artificial Intelligence Society’ – what researchers should know about AI

By Emma Norris, on 14 August 2019

By Candice Moore & Emily Hayes – University College London, UK

Artificial Intelligence (AI) in is on the rise globally. The largest investments in AI development have been reported in China and the United States, with members of the European Union not far behind. In the UK, AI start-ups have flourished and a £1 billion package of support has been offered to industry and academia, through Government and private sector investment. The UK Government has also recently announced that £250m will be spent on AI integration within the NHS.

Academic institutions are paying attention to the AI boom. For example,  MIT has announced it is ‘reshaping itself for the future’ by establishing the new MIT Stephen A. Schwarzman College of Computing. The College will address the challenges and opportunities afforded by the growing prevalence and sophistication of AI, such as the need for ethical and responsible technologies. Our own university UCL has recently established two new Centres for Doctoral Training related to AI, specialising in Foundational AI and AI-enabled Healthcare Systems.

Researchers working on AI applications with social and health implications must be equipped to make responsible judgements about the technology they are creating. As researchers on the Human Behaviour-Change Project, working to synthesise our understanding of behaviour change interventions using machine learning and AI, we wanted to explore these implications further.  We attended a talk on the ethics of using AI for decision making at the UCL Interaction Centre in May 2019 by Ronald Baecker, Professor Emeritus of Computer Science and author of Computers and Society, We summarised the discussion for the Digi-Hub blog.

What society must require from AI:

Baecker argued that, when evaluating AI systems we must think carefully about the consequences the output of the system might have for society, and we should evaluate the system based on the qualities we would expect a human carrying out the same task to have. Baecker drew a useful distinction between ‘consequential’ and ‘not-so-consequential’ AI, based on the types of decisions AI make.

Not-so-consequential AI:

Not-so-consequential AI systems have been around for decades, and are used to carry out simpler tasks such as speech, image and pattern recognition. These kinds of AI systems do not require such in-depth ethical assessment. Virtual assistants, such as Apple’s Siri and Amazon’s Alexa, are examples of not-so-consequential AI. For the most part, the consequences of misinterpreted voice commands are low risk, if not frustrating. Novel applications of not-so-consequential are increasing, and include the use of facial recognition to manage queues at the bar!

       Caption: Demonstration of bar queue face recognition system. Customers are assigned a number based on when they arrive in the queue 

Consequential AI:

More recently, AI systems have been created for a number of more complex tasks which have important societal consequences. Baecker gave a number of examples of such systems:

• Scanning CVs to weed out job applicants
• Evaluating risks children face within their families
• Informing judicial decisions about bail, sentencing and parole
• Diagnosing medical conditions
• Identifying faces in a crowd for police
• Caring for seniors
• Driving autonomous vehicles
• Guiding and directing drones and autonomous weapons

Baecker argues that we should expect consequential AI systems to have the same qualities as a human carrying out the same tasks. According to Baecker, these systems should have ‘common sense, empathy, sensitivity to others, compassion, and a sense of fairness and justice’.

For example, the Allegheny Family Screening Tool (AFST) uses predictive analytics to assign children a ‘risk score’, which aims to quantify the likelihood of the child being placed away from their home, based on their referral records. This risk score is used to guide clinicians when making judgements about the family’s case, with scores beyond a certain threshold being followed-up mandatorily. Arguably, a human carrying out this task should act in a fair manner and without bias. We should therefore expect the same from the system, as mistakes could have grave consequences for children and their families.

In his talk, Baecker gave a detailed set of criteria against which consequential AI should be judged:

1) Competence, dependability and reliability: AI systems should be expected to make reliable and dependable judgements. This is difficult to achieve using current artificial intelligence methods as machine-learning can be ‘greedy’, requiring a lot of training data to produce accurate results, and lacking in innate knowledge/common sense.

2) Openness, transparency and explainability: Currently, for machine-learning methods, openness, transparency and explainability can be difficult to achieve as information is represented in a distributed, less-penetrable manner. Since 2016, Darpa have been leading a major research project, Explainable Artificial Intelligence (XAI), which aims to resolve this issue.

3) Trustworthiness: For AI systems to be effectively used by humans, we need to have a good understanding of the level of trust we can put into them. Our colleague, Eva Jermutus, recently wrote a blog post on this issue. Baecker argues that human computer interaction and computer science research must be more collaborative to address this.

4) Responsibility and accountability: To use AI systems ethically, we need to think carefully about who is responsible if things go wrong. For example, who is accountable for complex systems, used and developed by multiple individuals? Should programmers pay for any wrongdoing, or is the onus on users to act responsibly?

5) Sensitivity, empathy, and compassion: In some cases, AI systems are required to appear compassionate to the user.  For example, when used to fulfil a caring role. Many systems designed for these purposes are anthropomorphised, or zoomorphised, to create this impression, However, this should be handled carefully as attempts to anthropomorphise robots often result in uncanny experiences.

6) Fairness, justice and ethical behaviour: Creating fair and just algorithms can be difficult, and AI systems often end up replicating human biases. For example, there have been debates over whether, COMPAS, an offender risk-assessment tool, is biased towards assessing black candidates as high risk.

Implications for researchers

• ‘AI systems are only as good as the data we put into them’ and we need to moderate decisions made by AI systems through human intervention. However, new technologies are being produced exponentially, and there are insufficient resources to evaluate them. Researchers should continue to develop efficient, ‘best practice’ evaluation frameworks that mitigate the issue of ‘explainability’ in AI. Adequate resources will be required to apply these frameworks in practice.
• Baecker argues that public knowledge about AI decision-making should be increased. This will help resolve unfounded distrust in AI, whilst empowering individuals to challenge the use of biased or inappropriate technology. Research findings must therefore be disseminated widely and clearly, without using jargon.
• The evaluation of AI decision-making is just one piece of a complex, ethical puzzle. Other considerations include the use and storage of personal data, and the automation of labour. Currently, it is uncommon for computer science courses to include ‘ethical AI’ on their syllabus, and Baecker concluded by stating that we should be training students and young researchers to be able to grapple with these issues.

Bios:

Candice Moore is a Research Assistant at UCL working on the Human Behaviour Change Project which aims to use AI methods to advance behavioural science research. She completed an MSc in Cognitive and Decision Science which involved designing and coding an experiment on causal perception. Previously she has worked on a variety of research projects in developmental psychology, including a large-scale educational intervention.

Emily Hayes is a Research Assistant at UCL working on the Human Behaviour Change Project, which aims to use AI methods to advance behavioural science research. She completed an MSc in Health Psychology at UCL in 2017 and her research examined older adult health literacy, in relation to childhood and adulthood socioeconomic position. Prior to joining the HBCP, Emily worked for a digital health start-up that provides an app for medication management and adherence. She has also worked on a Knowledge Transfer Partnership, investigating workplace wellbeing through qualitative and quantitative research. She is interested in digital health and behaviour-change interventions to reduce health inequalities.

Where are our pathways to change? eHealth weight management in young adults

By Emma Norris, on 1 August 2019

By Taylor Willmott – Griffith University, Australia

The magnitude of the obesity epidemic has led to a shift in focus from the clinical treatment of obesity to the development of prevention strategies that address the economic, environmental, sociocultural, and lifestyle-related causes of weight gain. Targeting high-risk groups with prevention interventions is assumed to have the greatest impact on the ever-rising prevalence of overweight and obesity. We are most likely to gain weight in our early twenties to mid-thirties, with incident obesity at a younger age associated with an increased risk of chronic disease and mortality in later adult life. The good news? If we adopt healthy lifestyle behaviours in young adulthood, we lower our future risk of developing obesity and associated chronic disease(s).

Current state-of-play

Our previously published review sought to evaluate the current state of evidence on eHealth weight management interventions targeting young adults (aged 18-35 years), with findings highlighting the limited evidence base for successful interventions. Of the 24 studies identified, eight reported positive weight-related outcomes, four reported mixed outcomes, and 12 did not report any significant changes in weight-related outcomes. To obtain a more nuanced understanding of this apparent lack of effectiveness, we assessed the extent of reported theory use in included interventions using the Theory Coding Scheme (TCS). Note: previously published review findings are available (open access) and our recent Digi-Hub blog post offers a summary of key findings.

Why assess theory use?

Previous reviews of interventions in this context have focused primarily on pooling primary outcome results to obtain one overall estimate of effectiveness. These reviews provide limited insight into how interventions are (or are not) achieving the desired outcomes. To obtain a more nuanced understanding, we must unpack the ‘black box’ and deconstruct these seemingly complex eHealth weight management interventions. Theory is a critical aspect of being able to deconstruct interventions—with theory we can identify the underlying mechanisms of action (and their associated behaviour change techniques) driving (or not) intervention outcomes. These theoretical links represent our pathways to change! For further discussions on the role and importance of theory, refer to this scoping review and/or our previously published agenda outlining ten theory development goals.

Improving the current state-of-play

In our latest review paper published in Health Psychology Review, we assessed the extent of reported theory use in eHealth weight management interventions targeting young adults according to the TCS. We then calculated an overall use of theory score based on total TCS item scores (1 = present, 0 = not present). Each study was categorised as having either weak, moderate, or strong levels of theory use based on total TCS scores (weak = 0-7; moderate = 8-15; and strong = 16-23). Overall, the mean total use of theory score was 6/23 (SD = 5; Min. = 0, Max. = 17); 17 studies were classified as having weak application of theory, five as moderate, and two as strong (see Table 1).

Mention of theory

The majority (N = 18) of studies mentioned theory (see Table 2); however, only nine studies referred to the referenced theory as a predictor of behaviour and presented evidence of the relationship between the theoretical constructs in the theory cited and the target behaviour(s). Of those studies referencing a theory, 50% (N = 9) were reportedly based on a single theory such as Social Cognitive Theory (SCT) or Self-Determination Theory (SDT), while the other 50% (N = 9) were reportedly based on a combination of predictors from multiple theories.

Application of theory

Most (N = 17) studies used theoretical predictors to select and/or develop intervention techniques (see Table 3). No study used theory to select intervention recipients, with only four tailoring intervention techniques to different sub-groups of the target population that varied on a theoretical construct at baseline. For example, in the TXT2BFiT intervention data collected from participants at baseline were used to create a staging algorithm based on the TTM to generate a personalised set of text messages tailored to each participants’ stage of change. In terms of linking intervention techniques with theoretical constructs, only four studies explicitly linked all intervention techniques to at least one theory-relevant construct. A further 12 explicitly linked at least one, but not all. Similarly, only four studies explicitly linked all theory-relevant constructs to at least one intervention technique, with a further 11 explicitly linking at least one, but not all. Nine studies linked a group or cluster of intervention techniques to a group or cluster of theoretical constructs.

Testing, building, and refining of theory

Only six studies measured theory-relevant constructs pre and postintervention; and only three reported the reliability and/or validity of the psychometric scales used to measure theory-relevant constructs (see Table 4). Of the six studies measuring theory pre and postintervention, four reported that the intervention led to a significant change in at least one theory-relevant construct in favour of the intervention. Five studies discussed intervention outcomes in relation to the theory mentioned, and one provided appropriate support for the stated theory. No study reported using intervention results to build and/or refine the theory upon which the intervention was based, or formulate suggestions for future refinement.

Where to from here?

While some interventions incorporated elements from a referenced theory, it was rare that all theoretical constructs within a particular theory were targeted in the intervention; and that valid measures of theoretical constructs were measured and tested pre and postintervention. The lack of studies linking theoretical constructs to intervention techniques, and testing theory in evaluations may be limiting effectiveness. Indeed, post-hoc analyses indicate that weight-related outcomes may be enhanced when at least one or more theoretical constructs are explicitly linked to an intervention technique, and when theoretical constructs are included in evaluations. Increases in theory application and reporting are therefore needed to assist the scientific research community in systematically identifying which theories work, for whom, how, why, and when; thereby delivering an advanced understanding of how best to apply theory to enhance intervention outcomes.

Read the full paper here.

Bio:

Taylor Willmott is a final year PhD candidate at Social Marketing @ Griffith, Griffith University. Taylor has held various research and teaching roles across leading higher education institutions in Australia including Queensland University of Technology, Griffith University, and the University of Queensland. The broad focus of Taylor’s research lies in applying marketing principles and techniques, combined with other evidence-based approaches, to create innovative behaviour change programs that benefit both individuals and society. Taylor’s research is multi-award winning, has been presented at world-renowned conferences, and published in top-tier academic journals such as the Journal of Marketing Management, International Journal of Consumer Studies, Health Education & Behavior, Health Psychology Review, and the Journal of Medical Internet Research.

Connect with Taylor:

LinkedIn: https://www.linkedin.com/in/taylorwillmott/

Twitter: https://twitter.com/TaylorWillmott

Email: t.willmott@griffith.edu.au

The Open Digital Health initiative – Extending the life of evidence-based digital health tools

By Emma Norris, on 16 July 2019

By Dominika Kwasnicka – SWPS University, Poland

The non-for-profit Open Digital Health initiative (www.opendigitalhealth.org) has started to encourage health scientists, practitioners, and technology developers to share evidence-based digital health tools. We are creating a searchable database of descriptions of evidence-based tools, apps, websites, devices, to allow digital health to grow faster, be cheaper and more transparent across the countries. And here is why are we setting it up.

A story about evidence-based tools that die too early…

Here is a story (and you may have heard a very similar one before): A group of researchers in the UK gets a funding grant to develop an app. Their aim is to promote physical activity in older people. They outsource a company to code the app. They review literature, design the app and test it with the users. They run a study with 150 people who use the app and with 150 who do not, and they show that this app was somehow effective. After a year, they publish an article and they put the app aside. It does not get much publicity or downloads, does not get updated and it dies after the funding period. Sad times. But does this sound familiar? 

An alternative ending: Open, Transparent and Shared Digital Health

And here is an alternative ending to the story you just heard: The same group of researchers is keen to share their work. They have the codes for the app, the content and all anonymised user data they’ve gathered. They don’t have time or money to take it forward but they list the descriptions of the app, code, content and data gathered on the Open Digital Health platform where other users can see it.
A group of researchers in Spain wants to promote physical activity in older people. They browse the Open Digital Health platform and locate the app created by the first group. They get in touch with the authors and ask for the permission to adapt the app considering appropriate licensing. They get it granted, translate the app to Spanish, use it with 300 people, get feedback, modify it, test it, and then show that the new app is even more effective than the original version. They publish the results, acknowledge the original authors and list the information about the app back on the Open Digital Health platform together with more information about new translated content.  Then a group of researchers in Chile finds the app on the platform and the story goes on…

Tell us what do you think!
We are passionate about digital health and we aim to make it more accessible for all. Sharing digital health tools will provide cost-effective opportunities for faster breakthroughs. We are asking you to fill in the survey (<2 minutes) to let us know if you want to join or give us your feedback: https://rsearch.eu/ls3/234982. Or simply leave a comment under this blog post and we will get back to you.

The Creators Team of the Open Digital Health initiative is led by Robbert Sanderman, Dominika Kwasnicka, Rik Crutzen, Gjalt Jorn Peters and Gill ten Hoor. We are inviting you to join us. Keep in touch if you have any comments/questions: info@digitalhealth.eu or dkwasnicka@swps.edu.pl

Questions

1.      What are your thoughts on the Open Digital Health initiative and sharing digital health tools (e.g., a way forward or no way I will not share any of my stuff!)

2.      Would you like to join us as a Supporter, Leader, or do you have tools you would like to share with us and list on the platform? If the answer is yes, please comment here or email us on: info@digitalhealth.eu or dkwasnicka@swps.edu.pl

3.      What would you like to see on this platform? We are open for your suggestions!

Bio:

Dr Dominika Kwasnicka (@dkwasnicka) is a Health Psychology researcher at SWPS University in Wroclaw, Poland. Her overall research interest is maintenance of behaviour change in public health. Her main research interest lies in exploring motives for behaviour maintenance, habits, self-regulation, and coping with behavioural barriers. She is interested in determining how environment and social networks influence human behaviour and how availability of physical and psychological resources shapes how people change and maintain health behaviours. Her research to date makes three key contributions to the field of Health Psychology and Behavioural Science by: (1) Integrating and summarising multiple theories of behaviour change maintenance looking at key predictors of maintained health behaviour change; (2) Testing and advancing these theoretical predictors in systematically developed studies and evidence-based interventions focusing on physical activity, diet and weight loss maintenance; (3) Contributing to the development of novel research methods employing novel research designs such as within-person N-of-1 studies and data prompted interviews; using most recent eHealth technologies. She is an active member of European Health Psychology Society and a Head Editor of the Practical Health Psychology Blog published worldwide in 20 different languages: www.practicalhealthpsychology.com, online publication for healthcare practitioners about cutting edge Health Psychology and how to apply it in practice. 

Get ‘em while they’re young! Combatting the obesity epidemic with e-Health

By Emma Norris, on 2 July 2019

By Taylor Willmott – Griffith University, Australia

We are most likely to gain weight during our early twenties to mid-thirties, with incident obesity associated with chronic disease and mortality in later adult life. Young adults with increasing body mass index (BMI) are twenty times more likely to develop metabolic syndrome over the subsequent 15 years of life. Young adults who can maintain a stable BMI over time have minimal progression of risk factors and lower incidence of metabolic syndrome. Identifying strategies that can support young adults in adopting a healthier lifestyle and maintaining a healthy weight over the long term is critical to the prevention of future incident obesity and associated conditions. Given the current generation of young adults are among the highest users of digital technologies, an eHealth-based approach offers potential for engaging large numbers of young adults in weight management. To explore this further, we sought to gather all of the available evidence on eHealth weight management interventions targeting young adults.

Five strategies to prevent weight gain in young adults

We screened over 1301 peer reviewed articles locating 24 weight management interventions delivered via eHealth and targeting young adults aged 18-35 years. Among the eight studies reporting positive weight-related outcomes, we were able to identify five strategies that can be applied in future programs to help young adults manage their weight.

(1) Goal setting and self-monitoring (self-regulation)

In our review, all studies reporting positive weight-related outcomes implemented some form of self-monitoring. For example, the HEYMAN intervention used a wearable physical activity tracker with an associated mobile phone app to assist participants in goal setting and self-monitoring. Most weight management interventions promote goal setting along with some form of self-monitoring. Typically, this involves participants recording their dietary and physical activity behaviours (and weight) and reviewing their performance in line with their previously set goals to evaluate progress. Self-monitoring and goal setting are fundamental for well-developed self-regulation skills. The idea behind self-regulation is that monitoring of one’s behaviour will lead to self-evaluation of progress made toward previously set goals with the resulting reinforcement (positive or negative) directing future behavioural performance. The use of technology can lessen the effort and time required for goal setting and self-monitoring and increase adherence. Given the perceived lack of time among young adults and a lack of adequate self-regulation skills, future programs should focus on developing goal setting and self-monitoring skills.

(2) Tailoring delivery of intervention content

Tailoring involves gathering and assessing personal data to devise a strategy that meets the specific needs of an individual. Tailored content and messages command greater attention, are processed more deeply by recipients, and are perceived as more likable than a generic message. With ready access to data provision and retrieval, the internet provides a powerful tool for tailoring weight management interventions. Tailoring can range from simple Web-based assessments and feedback to highly sophisticated interventions that are completely customised to each individual participant. Most of the studies in our review employed only simple forms of tailoring. The TXT2BFiT intervention included a more sophisticated level of tailoring with a staging algorithm used to generate a personalised set of SMS text messages that were tailored to whether a participant was in precontemplation, contemplation, preparation, action, or maintenance stages of change. Future programs should experiment with more sophisticated forms of tailoring to promote adherence and in turn effectiveness.

(3) Contact with a trained coach or interventionist

Support from a trained coach or interventionist who is seen as trustworthy, benevolent, and having expertise can enhance intervention outcomes. In our review, coaching calls, emails, chat forums, and social network sites were identified as potentially cost-effective means of delivering expert support at scale. For example, the TXT2BFiT intervention included five coaching calls led by a dietician skilled in motivational interviewing. Future programs should consider the potential benefits and disadvantages of different communication mediums for engaging young adults and delivering expert support at scale.

(4) Social support

In our review, three out of the eight studies reporting positive weight-related outcomes included social support. Social support was typically delivered via online chat forums or social network sites. For example, the CHOICES intervention created a study specific social network site to encourage discussion and interaction among participants. Similarly, the HEYMAN intervention used a combination of in-person (via group-based sessions) and Web-based (via a private Facebook group) social support to facilitate interaction among participants. With access to large existing (or new) networks of influencers, social networking sites provide an ideal platform for facilitating social support. Given there is evidence to suggest that social contacts and normative beliefs influence weight status and intentions for weight control in young adults, the facilitation of social support should be a key consideration in future programs.

(5) Behavioural prompts (nudges and reminders) and booster messages

Technology offers a feasible means of delivering strategies that promote behavioural maintenance; however, few interventions in our review incorporated behavioural prompts and/or booster messages. For example, the TXT2BFiT intervention reported positive weight-related outcomes and incorporated both behavioural prompts and booster messages. A low dose maintenance phase which included monthly SMS text messages, emails, and booster coaching calls was delivered after the initial 12-week intervention to promote behavioural maintenance and sustain outcomes. Given weight management requires healthy lifestyle choices to be made consistently over the long term, future programs should include both behavioural prompts and booster messages to support behavioural maintenance and ensure outcomes are sustained over the long term.

Where to from here?

eHealth offers a cost-effective and scalable means of engaging young adults in weight management. Findings from our review indicate that programs incorporating a combination of self-regulation skill development, tailored intervention content, contact with a trained interventionist, social support, and behavioural prompts and booster messages are likely to be more effective than programs not employing these strategies. If we can successfully “get ‘em while they’re young,” we can prevent excess weight gain in young adulthood and lower the risk of chronic disease in later adult life, thereby lessening the health burden on both the individual and society.

Read the full paper here.

Bio:

Taylor Willmott is a final year PhD candidate at Social Marketing @ Griffith, Griffith University. Taylor has held various research and teaching roles across leading higher education institutions in Australia including Queensland University of Technology, Griffith University, and the University of Queensland. The broad focus of Taylor’s research lies in applying marketing principles and techniques, combined with other evidence-based approaches, to create innovative behaviour change programs that benefit both individuals and society. Taylor’s research is multi-award winning, has been presented at world-renowned conferences, and published in top-tier academic journals such as the Journal of Marketing Management, International Journal of Consumer Studies, Health Education & Behavior, Health Psychology Review, and the Journal of Medical Internet Research.

Connect with Taylor:

LinkedIn: https://www.linkedin.com/in/taylorwillmott/

Twitter: https://twitter.com/TaylorWillmott

Email: t.willmott@griffith.edu.au