Internet-Based Health Interventions: What Works?

Many health behaviour interventions and treatment programs increasingly use web-based delivery platforms, capitalizing on the 80% of internet users in the US that search online resources for health information (1). Internet programs represent a relatively new way to deliver interventions designed to promote health behaviour change. Since the methods by which health promotion programs are delivered can impact the success of behaviour change outcomes, Dr. Thomas Webb, and colleagues from the University of Sheffield, sought to identify the characteristics of internet-based interventions that best promote successful health behaviour change (2).

In their paper, published in 2010 in the Journal of Medical Internet Research, Webb et al. identified 85 studies that used internet-based programs to support health behaviour change (2). Studies that only measured symptoms or outcomes (e.g. weight loss presumed to be the result of behavioural changes) were excluded, as were studies that focussed on behaviours unrelated to health. The studies were categorized using three different indices:

  • the behaviour change theory the intervention was based on
  • the specific behaviour change techniques used
  • the characteristics of the online delivery program

For each study, the researchers also calculated the effect size* for post-intervention behaviour differences.

Prior studies have developed taxonomies for categorizing behaviour change interventions based on theoretical origins (3) and intervention techniques (4). However, no such catalog existed for characterizing the mode of delivery for internet-based health behaviour interventions and was developed by the authors. Table 1 outlines the characteristics used by the authors for classifying the interventions. For example, an intervention that included a discussion forum would be tagged as allowing peer to peer access under communicative functions. An intervention that sent automated reminders would be tagged as providing follow-up messages under automated functions.

Table 1: Mode of Delivery Characteristics

Webb and colleagues found that basing programs on behaviour change theories enhances the effect size of the intervention. Other results suggest that including multiple behaviour change techniques and providing a variety of methods for participants to interact also improves the effectiveness of internet-based health behaviour change interventions.

Including multiple behaviour change techniques makes sense. Participants in an intervention, while they may be the same gender, within a similar age range, and earn a similar income, are still unique individuals. Multiple techniques will allow individuals to tailor the program to best suit their personal needs and to adapt the program as their needs may change. A key point to consider though, is at what point does a participant become overwhelmed by too many choices and give up?

Programs that included text messaging as an additional component of the intervention had the largest effect size of all characteristics measured. This illustrates to me the potential for mobile applications to have an overall stronger effect than programs tethered to a computer. While many of us do spend too many hours in front of a computer screen, the increased portability of a cell phone or other mobile device may make it easier to support a participant on demand. For example, reminders can be programmed to be sent at specific times and would not need to rely on the participant sitting at his or her computer to receive it.

Prompts to self-monitor, whether of the behaviour itself (e.g. physical activity) or of its outcome (e.g. weight) had relatively small effects. This finding surprised me, as self-monitoring has been found by other studies to be an effective behaviour change technique (5). Perhaps the low success rate of prompts to self-monitor is related to the lack of portability of a desktop based intervention; using a mobile device to track behaviour can give more “instant” feedback compared to the requirement of logging onto a web site on a desktop computer.

A limitation to consider when interpreting the results of this paper is that no assessment of web site usability from the original studies was made. Although beyond the scope of the review by Webb et al., (such an assessment may not have been possible), web site naviagation and other usability criteria may have a significant effect on the outcome of an internet-based intervention. Poor usability could potentially lead to subject frustration, reduced compliance, and poor adherance to the program, perhaps even contributing to drop-out.

These study findings have important implications for the future development of internet-based interventions that support health behaviour change. Presently, 25% of internet users track their weight, physical activity levels, food intake or other health measures (1). As this proportion of the population increases, it becomes increasingly important to ensure the design of programs that support behaviour change is guided by evidence based research. Webb and colleagues provide a starting framework to help optimize the effectiveness of health behaviour change interventions delivered online, with the additional potential for adapting the evaluation to mobile platforms.

References

1. Fox, S. (2001) The Social Life of Health Information, 2011: Summary of Findings (Pew Report) http://www.pewinternet.org/Reports/2011/Social-Life-of-Health-Info/Summary-of-Findings.aspx

ResearchBlogging.org2. Webb TL, Joseph J, Yardley L, & Michie S (2010). Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of medical Internet research, 12 (1) PMID: 20164043

The full article is available for free through PubMed and I encourage anyone interested to read it in full.

3. Michie Susan, Prestwich Andrew. (2010) Are interventions theory-based? Development of a theory coding scheme. Health Psychol. Jan;29(1):1–8 PMID: 20063930

4. Abraham Charles, Michie Susan. (2008)A taxonomy of behavior change techniques used in interventions. Health Psychol. May;27(3):379–87 PMID: 18624603

5. van Achterberg T, Huisman-de Waal G, Ketelaar N, et al. (2010) How to promote healthy behaviours in patients? An overview of evidence for behaviour change techniques. Health promotion international. 26(2):148-62 PMID: 20739325

* Effect size refers to an estimate of the strength of the outcome; in this study it was calculated as the difference between the means of the experimental and control groups divided by the pooled standard deviation (Hedges g).

Image by Flick user daoro

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