Scholarlink

<p>This systematic review synthesized 61 dynamically tailored eHealth interventions for chronic disease management from 117 papers. Tailoring strategies varied in scope and complexity, with most targeting physical activity (87%) and nutrition (43%), while nearly three-quarters also integrated contextual, emotional, or physiological variables. Physical activity was often objectively measured (60%), but dietary intake remained self-reported (100%). Disease-specific biofeedback, such as glucose or blood pressure monitoring, was rare. Tailoring was predominantly rule-based (74%), though data-driven methods like machine learning (13%) are emerging. Most interventions used text-based delivery and drew on behavior change theory, particularly goal setting, self-monitoring, and feedback. While many showed positive within-group outcomes, benefits over controls were inconclusive. Progress within the field requires: (1) multidisciplinary development with rationale, (2) transparent reporting using structured frameworks, and (3) innovative evaluation designs to disentangle multi-component interventions. Strengthening methodological foundations is essential to unlock potential for delivering tailored lifestyle support in chronic disease care.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Systematic review of dynamically tailored eHealth interventions targeting physical activity and healthy diet in chronic disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

查看全文
  • E. A. G. Hietbrink,
  • C. Lansink,
  • G. D. Laverman,
  • M. M. R. Vollenbroek-Hutten,
  • A. Middelweerd,
  • M. Tabak

摘要

This systematic review synthesized 61 dynamically tailored eHealth interventions for chronic disease management from 117 papers. Tailoring strategies varied in scope and complexity, with most targeting physical activity (87%) and nutrition (43%), while nearly three-quarters also integrated contextual, emotional, or physiological variables. Physical activity was often objectively measured (60%), but dietary intake remained self-reported (100%). Disease-specific biofeedback, such as glucose or blood pressure monitoring, was rare. Tailoring was predominantly rule-based (74%), though data-driven methods like machine learning (13%) are emerging. Most interventions used text-based delivery and drew on behavior change theory, particularly goal setting, self-monitoring, and feedback. While many showed positive within-group outcomes, benefits over controls were inconclusive. Progress within the field requires: (1) multidisciplinary development with rationale, (2) transparent reporting using structured frameworks, and (3) innovative evaluation designs to disentangle multi-component interventions. Strengthening methodological foundations is essential to unlock potential for delivering tailored lifestyle support in chronic disease care.