Effectiveness of the Components of a Digital Multiple Health Behavior Change Intervention Among Individuals Seeking Help Online (Coach): Factorial Randomized Trial
Clinical Snapshot
PICO Framework
| P — Population | Adults (≥18 years) from the general Swedish population seeking help online to change health behaviors, with at least one unhealthy behavior |
| I — Intervention | Digital health behavior change intervention with 6 components: screening/feedback, goal-setting/planning, motivation, skills/know-how, mindfulness, and self-authored SMS text messages |
| C — Comparator | Factorial design comparing presence vs absence of each component (2^6 = 64 possible combinations) |
| O — Outcomes | Weekly alcohol consumption, heavy episodic drinking frequency, daily fruit/vegetable consumption, weekly moderate-to-vigorous physical activity, and 4-week point-prevalence smoking cessation |
Bottom Line
This large factorial randomized trial demonstrates that screening/feedback components are most effective in digital health behavior change interventions, with goal-setting and motivation also showing benefits. The study's innovative design reveals that combining screening/feedback with motivation enhancement produces synergistic effects on fruit and vegetable consumption. While effect sizes are modest, the population-level impact could be substantial given the scalability of digital interventions. The findings support prioritizing personalized feedback mechanisms in digital health platforms. However, longer-term follow-up and cost-effectiveness data are needed before widespread implementation. Australian clinicians should consider these components when recommending digital health behavior change tools, particularly for patients seeking to address multiple lifestyle factors simultaneously.
Key Findings
P Value: Probability of effect >99.9% for fruit/vegetable outcomes, 94.2% for heavy drinking reduction
Effect Size: Fruit/vegetable consumption increased by 0.17 portions/day at 2 months, 0.13 portions/day at 4 months
Primary Outcome: Screening/feedback component showed strongest effects across multiple behaviors
Nnt Or Sensitivity: Heavy episodic drinking frequency reduced by 9% (IRR 0.91, CoI 0.81-1.03)
Confidence Interval: 2-month fruit/vegetable increase: 0.09-0.25 portions/day; 4-month: 0.04-0.21 portions/day
Clinical Application
High feasibility given digital delivery format, though requires technological infrastructure and user engagement Highly relevant to Australian digital health initiatives and preventive care strategies. Could inform development of PBS-funded digital therapeutics and support RACGP guidelines for lifestyle counseling Adults seeking digital health behavior change interventions, particularly those with multiple unhealthy behaviors
Abstract
BACKGROUND: Extant digital multiple health behavior change interventions have shown promise in various populations; however, evidence for a broader approach among the general population is lacking. Moreover, existing interventions often contain several components but are typically assessed as a whole, meaning it remains unclear to what extent individual components contribute to intervention effects and how they may interact to influence health outcomes. OBJECTIVE: This study estimates the effects of 6 components of a digital health behavior change intervention on alcohol, diet, physical activity, and smoking outcomes among individuals searching for help online. METHODS: A double-blind randomized factorial trial design with 6 two-level factors was used. Adults from the general public in Sweden who were seeking help to change their behaviors were recruited through web searches and social media. Participants were eligible if they were 18 years or older and had at least one health behavior classified as unhealthy. Effects of 6 components were estimated: screening/feedback, goal-setting/planning, motivation, skills/know-how, mindfulness, and self-authored SMS text messages. Primary outcomes were weekly alcohol consumption and frequency of heavy episodic drinking, average daily fruit and vegetable consumption, weekly moderate-to-vigorous physical activity, and 4-week point-prevalence smoking. RESULTS: A total of 5419 individuals were randomized. Overall, the screening/feedback component was the most effective for changing health behaviors, along with goal-setting/planning and motivation to change. In particular, there was evidence that screening/feedback increased average daily portions of fruit and vegetables at 2 months (mean difference 0.17, compatibility interval [CoI] 0.09-0.25, probability of effect [POE] >99.9%) and at 4 months (mean difference 0.13, CoI 0.04-0.21, POE 99.9%) and reduced the frequency of heavy episodic drinking at 4 months (incidence rate ratio 0.91, CoI 0.81-1.03, POE 94.2%). Components also interacted to further improve health outcomes, most notably the combination of screening/feedback with motivation to change, which further increased fruit and vegetable consumption (2 months: mean difference 0.20, CoI 0.09-0.30, POE >99.9%; 4 months: mean difference 0.17, CoI 0.05-0.29, POE 99.8%). CONCLUSIONS: The results from this study contribute to the development of more effective interventions by providing novel insights into the effects of individual and pairwise components of complex digital health behavior change interventions. TRIAL REGISTRATION: ISRCTN Registry ISRCTN16420548; http://www.isrctn.com/ISRCTN16420548. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2022-061024.
References
- 1.Crawford, J., Åsberg, K., Blomqvist, J., Lundgren, O., Henriksson, H., Henriksson, P., Bendtsen, P., Löf, M., & Bendtsen, M. (2026). Effectiveness of the Components of a Digital Multiple Health Behavior Change Intervention Among Individuals Seeking Help Online (Coach): Factorial Randomized Trial. Journal of Medical Internet Research, 28(4). https://doi.org/10.2196/88881
Related Research
The Journal of rheumatology
Examining the Role of Wearables in Inflammatory Arthritis Care: A Narrative Literature Review
3 Aug 2026
Journal of medical Internet research
Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care
2 Aug 2026
Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
2 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service