Effectiveness of the Self-Directed mHealth Exercise Intervention re.flex in Patients With Knee Osteoarthritis: Randomized Controlled Trial.

IF 6 2区医学 Q1 HEALTH CARE SCIENCES & SERVICES

Journal of Medical Internet Research Pub Date : 2025-10-09 DOI:10.2196/71558

Valerie Dieter, Peter Martus, David Seißler, Lina Maria Serna-Higuita, Pia Janssen, Inga Krauss

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引用次数: 0

Abstract

Background: About 1 in 2 patients with knee osteoarthritis (OA) receives a referral or recommendation for exercise. Digital health applications could counteract this undersupply.

Objective: We aimed to investigate the effectiveness of a 12-week self-directed mobile health exercise intervention (re.flex) when used in addition to usual care compared to a control group receiving usual care only on pain reduction and improvement in physical function in patients with knee OA.

Methods: This monocentric, 2-arm, randomized controlled parallel-group trial included patients from Germany with moderate to severe knee OA. Participants were mainly recruited via newspapers. Randomization was 1:1 into an intervention group (re.flex+usual care) and a control group (usual care) using computer-generated blocks. Participants were unmasked to group assignment. The re.flex group conducted a 12-week self-directed app-based and sensor-assisted exercise program with 3 sessions per week in addition to usual care. Primary outcomes were OA-specific knee pain and physical function (using the subscales pain and activities in daily living of the Knee Osteoarthritis Outcome Score, 0-100) at 3 months. Secondary outcomes included adherence and safety. Multiple imputation was used to account for missing data. Intervention effects were calculated using a baseline-adjusted analyses of covariance (ANCOVA). Bonferroni correction with an alpha level of .025 was applied.

Results: Between January 25, 2023, and August 11, 2023, a total of 195 participants were enrolled. Of them, 98 participants were allocated to re.flex, and 97 participants to usual care. The primary analysis included 194 participants. The mean age was 61.9 (SD 7.7) years, and the majority were female (132/194, 68%). Pain reduction was significantly larger in re.flex than in usual care, with an adjusted mean difference between study groups of 4.8 (95% CI 0.7-8.9; P=.02; Cohen d=0.35) points. Improvement in physical function was not statistically significant (beta coefficient [β]=3.9 points, 95% CI 0.0-7.9, P=.049). A total of 12 adverse events were linked to re.flex, none of which were serious. Participants adhered to 77% (2705/3528) of all scheduled exercise sessions.

Conclusions: The self-directed sensor-based mobile health exercise intervention re.flex demonstrates superiority over usual care for pain reduction and justifies this kind of intervention as an alternative exercise delivery mode for patients with knee OA.

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自主移动健康运动干预反射在膝骨关节炎患者中的有效性：随机对照试验

背景：大约1 / 2的膝骨关节炎（OA）患者接受转诊或推荐运动。数字健康应用可以弥补这种供应不足。目的：我们旨在研究在常规护理之外进行为期12周的自我指导移动健康运动干预（反射）的有效性，并与仅接受常规护理的对照组进行比较，以减轻膝关节OA患者的疼痛和改善身体功能。方法：这项单中心、双臂、随机对照平行组试验纳入了来自德国的中度至重度膝关节OA患者。参与者主要通过报纸招募。采用计算机生成的分组，按1:1随机分为干预组（反射+常规护理）和对照组（常规护理）。参与者被揭下面具进行小组分配。reflect .flex组进行了一项为期12周的基于应用程序和传感器辅助的自我指导锻炼计划，除常规护理外，每周进行3次锻炼。主要结局是3个月时oa特异性膝关节疼痛和身体功能（使用膝关节骨关节炎结局评分的疼痛和日常生活活动亚量表，0-100）。次要结局包括依从性和安全性。使用多重输入来解释缺失的数据。采用基线校正协方差分析（ANCOVA）计算干预效果。的Bonferroni校正。应用了025。结果：在2023年1月25日至2023年8月11日期间，共有195名参与者入组。其中98名参与者被分配到反射治疗组，97名参与者被分配到常规治疗组。初步分析包括194名参与者。平均年龄61.9岁（SD 7.7），以女性居多（132/194,68%）。反射治疗的疼痛减轻明显大于常规治疗，研究组间调整后的平均差异为4.8点（95% CI 0.7-8.9； P= 0.02; Cohen d=0.35）。身体功能的改善无统计学意义（β系数[β]=3.9点，95% CI为0.0-7.9，P= 0.049）。共有12个不良事件与反射有关，没有一个是严重的。参与者坚持77%（2705/3528）的计划锻炼。结论：基于自我导向传感器的移动健康运动干预refi .flex在减轻疼痛方面优于常规护理，证明了这种干预作为膝关节OA患者的一种替代运动传递模式的合理性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Medical Internet Research 医学-卫生保健

CiteScore

14.40

自引率

5.40%

发文量

654

审稿时长

1 months

期刊介绍： The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.