AI Insight
This is a study protocol for an N-of-1 trial testing a mobile health intervention designed to improve physical activity and nutrition in cancer patients aged 70 and older. The 12-week study uses a single-subject ABA' design, combining daily ecological momentary assessments via smartphone app with accelerometer measurements to track individual behavioral patterns. The intervention phase includes personalized messages, weekly phone calls, and behavior change techniques based on the Theory of Planned Behavior, with data analyzed through piecewise regression models and qualitative interviews.
Why it matters
Older cancer patients frequently fail to meet recommended physical activity and dietary guidelines, which can affect treatment outcomes and quality of life. This personalized approach using real-time data collection and tailored interventions could provide insights into effective strategies for promoting sustainable health behaviors in this vulnerable population.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
BACKGROUND: Older adults with cancer often struggle to achieve recommended levels of physical activity and dietary intake. Ecological momentary assessment combined with accelerometry can provide insights into the temporal dynamics of psychological and behavioral processes at the individual level, such as motivation towards health behaviors. OBJECTIVE: This N-of-1 study aims to improve physical activity and nutritional behaviors among older patients with cancer using an mHealth behavioral intervention. METHODS: A single-subject ABA’ design will be employed among older patients with cancer ([≥] 70 years). A 2-week baseline phase (A) will be followed by an 8-week intervention phase (B) and a two-week withdrawal phase (A’). Throughout all these phases, participants will complete a daily data collection protocol combining ecological momentary assessment questionnaires and an ActiGraph wGT3X-BT accelerometer worn on the waist to measure physical activity. Ecological momentary assessment questionnaires will be delivered via a digital application to collect information on nutritional behavior, fatigue, and motivational constructs based on the Theory of Planned Behavior. The intervention (B) will consist of an mHealth intervention based on behavior change techniques, delivered via weekly calls, personalized messages, and a digital application. Data will be analyzed using piecewise and segmented regression models. In addition, a semi-structured interview will be conducted to assess patient experience. These qualitative data will help identify contextual factors, such as treatment-related side effects or variations in health status, that may have influenced behavior change and participation in data collection. CONCLUSION: This N-of-1 study explores intra-individual behavioral dynamics using intensive longitudinal data rather than testing a strictly reversible intervention effect. The mHealth intervention is based on behavior change techniques and tailored to each patient, with adjustments made based on repeated daily assessments in a real-world setting using a wGT3X-BT accelerometer and ecological momentary assessment questionnaires. The results will contribute to the evidence base for mHealth interventions designed to promote sustained physical activity and dietary intake among older adults with cancer.