Learning from community-led and co-designed m-health interventions
Authors: Mitch J Duncan, Gregory S Kolt
Journal: The Lancet Digital Health (2019)

In The Lancet Digital Health, Cliona Ni Mhurchu and colleagues1Ni Mhurchu C Te Morenga L Tupai-Firestone R et al.A co-designed mHealth programme to support healthy lifestyles in Māori and Pasifika people in New Zealand ([email protected]@): a cluster-randomised controlled trial.Lancet Digital Health. 2019; (published online Sept 17)https://doi.org/10.1016/S2589-7500(19)30130-XSummary Full Text Full Text PDF PubMed Scopus (23) Google Scholar evaluate their co-designed intervention (the [email protected]@ mHealth intervention) to improve adherence to health-related guidelines relating to smoking, nutrition, alcohol, and physical activity behaviours. The study used a cluster-randomised controlled trial (RCT) design with 69 communities, in which 1451 adults were randomly assigned to either the intervention or the control group. At 12 weeks, the two study groups did not differ in the proportions of participants adhering to the behavioural guidelines, but both groups showed increased adherence to the guidelines. Although, at first glance, this null finding is disappointing, it highlights and reinforces the many challenges facing research in the mHealth field. Notably, the published null findings will help to reduce publication bias. A strength of the [email protected]@ study was the co-design process.1Ni Mhurchu C Te Morenga L Tupai-Firestone R et al.A co-designed mHealth programme to support healthy lifestyles in Māori and Pasifika people in New Zealand ([email protected]@): a cluster-randomised controlled trial.Lancet Digital Health. 2019; (published online Sept 17)https://doi.org/10.1016/S2589-7500(19)30130-XSummary Full Text Full Text PDF PubMed Scopus (23) Google Scholar In New Zealand, substantial health inequalities exist between the Māori, Pasifika, and New Zealand European populations, and the cultural beliefs and practices of Māori and Pasifika people must be considered and addressed in health care. Co-design of the intervention with Māori and Pasifika communities was used to understand the needs and goals of these community groups, and this approach was combined with behaviour change theory and techniques to develop the intervention. The authors are to be commended for this process, and this co-design approach even guided how the gamification aspects of the intervention were delivered. The importance of this co-design process, in addition to the community-based cluster coordinators who led recruitment for the study, were key to achieving buy in from participants. This approach to community-based and community-led research is an exemplar for other research in the mHealth and eHealth area. Despite this community-led approach, engagement with the intervention, measured as the number of participants setting goals or reading intervention messages, was low.1Ni Mhurchu C Te Morenga L Tupai-Firestone R et al.A co-designed mHealth programme to support healthy lifestyles in Māori and Pasifika people in New Zealand ([email protected]@): a cluster-randomised controlled trial.Lancet Digital Health. 2019; (published online Sept 17)https://doi.org/10.1016/S2589-7500(19)30130-XSummary Full Text Full Text PDF PubMed Scopus (23) Google Scholar No detailed measures of intervention engagement were presented, although high non-usage attrition is not uncommon in mHealth interventions.2Eysenbach G The law of attrition.J Med Internet Res. 2005; 7: e11Crossref PubMed Scopus (1640) Google Scholar But these participant engagement actions are only one part of emerging models of user engagement.3Short CE Rebar AL Plotnikoff RC Vandelanotte C Designing engaging online behaviour change interventions: a proposed model of user engagement.Eur Health Psychol. 2015; 17: 32-38Google Scholar Our work suggests that different usage patterns and a higher rate of non-attrition are observed in eHealth interventions that use approaches that vary from the tightly-controlled nature of RCTs that include characteristics such as face-to-face assessments to study designs that more closely align with real-world conditions.4Vandelanotte C Duncan MJ Kolt GS et al.More real-world trials are needed to establish if web-based physical activity interventions are effective.Br J Sports Med. 2018; (published online July 3.)DOI:10.1136/bjsports-2018-099437Crossref Scopus (23) Google Scholar Intervention evaluations under conditions that are more similar to how the public use and engage with such programs in the real world are needed. This requirement presents several challenges. The first of these challenges is in the use of RCTs to evaluate mHealth interventions. RCTs have guided much medical evidence and have many uses, but this method should be used as only one of the ways to evaluate mHealth interventions. Under some circumstances, alternative designs, such as those used in implementation science might be more appropriate.5Brown CH Curran G Palinkas LA et al.An overview of research and evaluation designs for dissemination and implementation.Annu Rev Public Health. 2017; 38: 1-22Crossref PubMed Scopus (249) Google Scholar These designs could also allow interventions to be evaluated at scale, which is important when the often-cited benefit of eHealth and mHealth interventions is their potential reach. Related to this point is that participants in the [email protected]@ mHealth intervention were recruited via existing community groups, and trial participants could invite friends to participate in the intervention. This approach partly enables evaluation of the intervention under conditions that align with how individuals use mHealth interventions in their daily life. Improvements in the control group are not uncommon in behavioural interventions.6Waters L George AS Chey T Bauman A Weight change in control group participants in behavioural weight loss interventions: a systematic review and meta-regression study.BMC Med Res Methodol. 2012; 12: 120Crossref PubMed Scopus (26) Google Scholar In the [email protected]@ trial, the authors note that this improvement in outcomes of the control group could be due to self-reporting bias associated with outcome measurement, or it could be because the control group had regular contact from the cluster coordinator. This contact served several objectives, including maintaining the community relationships and promoting completion of the 12-week assessment and, in combination with the cultural relevance of the intervention, likely contributed to the follow-up completion of 84% at 12 weeks, which is high for the mHealth field. However, a question integral to the future of this field is whether a control group on a waiting list is the best comparator in trials like the [email protected]@ study. The answer to this question depends on the aim of the trial, and expert recommendations7Freedland KE King AC Ambrosius WT et al.The selection of comparators for randomized controlled trials of health-related behavioral interventions: recommendations of an NIH expert panel.J Clin Epidemiol. 2019; 110: 74-81Summary Full Text Full Text PDF PubMed Scopus (61) Google Scholar from this year present a useful framework to help guide this decision. The inclusion criteria for the [email protected]@ mHealth intervention study did not consider levels of engagement in the unhealthy risk behaviours that were targeted in the intervention. Consequently, the baseline attainment of the outcome (around 45%) was higher than anticipated (30%) when designing the trial, which might have influenced the ability of the study to detect a difference between groups. Inclusion criteria on baseline behaviours might have partially resolved this issue, although the effect of this approach on participation within each cluster is unknown. The primary outcome measure was based on the sum of four dichotomous indicators for each of the target behaviours (meeting guidelines or not meeting guidelines). This approach does not credit participants for incremental improvements in behaviour that could be important for health (such as increasing from 0 min of activity per day to 20 min per day). Other approaches to creating a single outcome that do incorporate incremental improvements in several behaviours are available,8Drake BF Quintiliani LM Sapp AL et al.Comparing strategies to assess multiple behavior change in behavioral intervention studies.Transl Behav Med. 2013; 3: 114-121Crossref PubMed Scopus (17) Google Scholar and they might have provided different insights into the efficacy of the intervention; that said, these approaches are not as easily interpreted. The absence of a significant between-group difference in the primary outcome is likely not what the research team envisaged when they initiated the trial. However, the [email protected]@ mHealth intervention highlights many interesting and thought-provoking issues for the broader mHealth field to consider. In doing so, this study makes a useful contribution to the field. MJD is supported by a Career Development Fellowship (APP1141606) from the National Health and Medical Research Council. GSK declares no competing interests. A co-designed mHealth programme to support healthy lifestyles in Māori and Pasifika peoples in New Zealand ([email protected]@): a cluster-randomised controlled trialThe [email protected]@ mobile health programme did not improve adherence to health-related behaviour guidelines amongst Māori and Pasifika individuals. Full-Text PDF Open Access