This paper illustrates a rigorous approach to developing digital interventions using an evidence-, theory- and person-based approach. Intervention planning included a rapid scoping review that identified cancer survivors’ needs, including barriers and facilitators to intervention success. Review evidence ( N = 49 papers) informed the intervention’s Guiding Principles, theory-based behavioural analysis and logic model. The intervention was optimised based on feedback on a prototype intervention through interviews ( N = 96) with cancer survivors and focus groups with NHS staff and cancer charity workers ( N = 31). Interviews with cancer survivors highlighted barriers to engagement, such as concerns about physical activity worsening fatigue. Focus groups highlighted concerns about support appointment length and how to support distressed participants. Feedback informed intervention modifications, to maximise acceptability, feasibility and likelihood of behaviour change. Our systematic method for understanding user views enabled us to anticipate and address important barriers to engagement. This methodology may be useful to others developing digital interventions.

BackgroundIn designing digital interventions for healthcare, it is important to understand not just whether interventions work but also how and for whom—including whether individual intervention components have different effects, whether a certain usage threshold is required to change behavior in each intervention and whether usage differs across population subgroups.PurposeWe investigated these questions using data from a large trial of the digital PRimary care trial of a website based Infection control intervention to Modify Influenza-like illness and respiratory tract infection Transmission) (PRIMIT) intervention, which aimed to reduce respiratory tract infections (RTIs) by increasing hand hygiene behavior.MethodBaseline and follow-up questionnaires measured behaviors, intentions and attitudes in hand hygiene. In conjunction with objective measures of usage of the four PRIMIT sessions, we analysed these observational data to examine mechanisms of behavior change in 8993 intervention users.ResultsWe found that the PRIMIT intervention changed behavior, intentions and attitudes, and this change was associated with reduced RTIs. The largest hand hygiene change occurred after the first session, with incrementally smaller changes after each subsequent session, suggesting that engagement with the core behavior change techniques included in the first session was necessary and sufficient for behavior change. The intervention was equally effective for men and women, older and younger people and was particularly effective for those with lower levels of education.ConclusionsOur well-powered analysis has implications for intervention development. We were able to determine a ‘minimum threshold’ of intervention engagement that is required for hand hygiene change, and we discuss the potential implications this (and other analyses of this type) may have for further intervention development. We also discuss the application of similar analyses to other interventions.

Background There is modest evidence that exercise referral schemes increase physical activity in inactive individuals with chronic health conditions. There is a need to identify additional ways to improve the effects of exercise referral schemes on long-term physical activity. Objectives To determine if adding the e-coachER intervention to exercise referral schemes is more clinically effective and cost-effective in increasing physical activity after 1 year than usual exercise referral schemes. Design A pragmatic, multicentre, two-arm randomised controlled trial, with a mixed-methods process evaluation and health economic analysis. Participants were allocated in a 1 : 1 ratio to either exercise referral schemes plus e-coachER (intervention) or exercise referral schemes alone (control). Setting Patients were referred to exercise referral schemes in Plymouth, Birmingham and Glasgow. Participants There were 450 participants aged 16–74 years, with a body mass index of 30–40 kg/m2, with hypertension, prediabetes, type 2 diabetes, lower limb osteoarthritis or a current/recent history of treatment for depression, who were also inactive, contactable via e-mail and internet users. Intervention e-coachER was designed to augment exercise referral schemes. Participants received a pedometer and fridge magnet with physical activity recording sheets, and a user guide to access the web-based support in the form of seven ‘steps to health’. e-coachER aimed to build the use of behavioural skills (e.g. self-monitoring) while strengthening favourable beliefs in the importance of physical activity, competence, autonomy in physical activity choices and relatedness. All participants were referred to a standard exercise referral scheme. Primary outcome measure Minutes of moderate and vigorous physical activity in ≥ 10-minute bouts measured by an accelerometer over 1 week at 12 months, worn ≥ 16 hours per day for ≥ 4 days including ≥ 1 weekend day. Secondary outcomes Other accelerometer-derived physical activity measures, self-reported physical activity, exercise referral scheme attendance and EuroQol-5 Dimensions, five-level version, and Hospital Anxiety and Depression Scale scores were collected at 4 and 12 months post randomisation. Results Participants had a mean body mass index of 32.6 (standard deviation) 4.4 kg/m2, were referred primarily for weight loss and were mostly confident self-rated information technology users. Primary outcome analysis involving those with usable data showed a weak indicative effect in favour of the intervention group (n = 108) compared with the control group (n = 124); 11.8 weekly minutes of moderate and vigorous physical activity (95% confidence interval –2.1 to 26.0 minutes; p = 0.10). Sixty-four per cent of intervention participants logged on at least once; they gave generally positive feedback on the web-based support. The intervention had no effect on other physical activity outcomes, exercise referral scheme attendance (78% in the control group vs. 75% in the intervention group) or EuroQol-5 Dimensions, five-level version, or Hospital Anxiety and Depression Scale scores, but did enhance a number of process outcomes (i.e. confidence, importance and competence) compared with the control group at 4 months, but not at 12 months. At 12 months, the intervention group incurred an additional mean cost of £439 (95% confidence interval –£182 to £1060) compared with the control group, but generated more quality-adjusted life-years (mean 0.026, 95% confidence interval 0.013 to 0.040), with an incremental cost-effectiveness ratio of an additional £16,885 per quality-adjusted life-year. Limitations A significant proportion (46%) of participants were not included in the primary analysis because of study withdrawal and insufficient device wear-time, so the results must be interpreted with caution. The regression model fit for the primary outcome was poor because of the considerable proportion of participants [142/243 (58%)] who recorded no instances of ≥ 10-minute bouts of moderate and vigorous physical activity at 12 months post randomisation. Future work The design and rigorous evaluation of cost-effective and scalable ways to increase exercise referral scheme uptake and maintenance of moderate and vigorous physical activity are needed among patients with chronic conditions. Conclusions Adding e-coachER to usual exercise referral schemes had only a weak indicative effect on long-term rigorously defined, objectively assessed moderate and vigorous physical activity. The provision of the e-coachER support package led to an additional cost and has a 63% probability of being cost-effective based on the UK threshold of £30,000 per quality-adjusted life-year. The intervention did improve some process outcomes as specified in our logic model. Trial registration Current Controlled Trials ISRCTN15644451. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 63. See the NIHR Journals Library website for further project information.