This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Cancer Survivorship (334) Fitness Trackers and Smart Pedometers/Accelerometers (665) Nutrition, Physical Activity, Healthy Lifestyle for Cancer Patients and Survivors (184) Wearable Devices and Sensors (1133) Mobile Health (mhealth) (3464)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/74347, first published .
The Effectiveness of Wearable Electronic Device System–Supported Physical Activity Programs for Cancer Survivors: Meta-Analysis of Randomized Controlled Trials

The Effectiveness of Wearable Electronic Device System–Supported Physical Activity Programs for Cancer Survivors: Meta-Analysis of Randomized Controlled Trials

The Effectiveness of Wearable Electronic Device System–Supported Physical Activity Programs for Cancer Survivors: Meta-Analysis of Randomized Controlled Trials

Authors of this article:

Zezhang Wang1, 2 Author Orcid Image ;   Yunhuan Li3 Author Orcid Image ;   Qian Wang1, 2 Author Orcid Image ;   Yonglin Su1, 2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Center of Rehabilitation Medicine, West China Hospital of Sichuan University, No 37, Guoxue Alley, Wuhou District, Chengdu, Sichuan, China

    2Rehabilitation Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    3Department of Nursing, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, China

    *these authors contributed equally

    Corresponding Author:

    Yonglin Su, PhD


    Background: As cancer is increasingly regarded as a chronic disease, it is essential to support cancer survivors’ self-management and enhance their quality of life (QoL). Although a physically active lifestyle can help alleviate symptom burden, improve QoL, and even benefit survival among cancer survivors, many remain physically inactive. Wearable electronic device systems (WEDSs) have become increasingly integrated into daily life and may offer a potential solution to promote physical activity (PA) and improve QoL in this population. However, existing findings remain modest and inconclusive.

    Objective: This meta-analysis aims to evaluate (1) the effects of WEDS-supported PA programs on improving PA, sedentary behavior, BMI, and QoL in cancer survivors; and (2) the effects of various types of these interventions.

    Methods: A comprehensive literature search was conducted across PubMed, Embase, Web of Science, CENTRAL, and MEDLINE from database inception through July 31, 2024. Two authors independently screened the articles, extracted the data, and evaluated the methodological quality of the included studies using the Cochrane Risk-of-Bias tool 2. Data synthesis was performed using R Studio. The effects of the interventions were determined by calculating standard mean differences (SMDs) and 95% CIs, while heterogeneity was assessed using I² statistics and P values. Subgroup analysis was conducted to assess whether the effects differed by the formats of the partnering tools and the duration of the intervention. Sensitivity analysis was performed using the one-study-out method to evaluate the robustness of the results, and the Egger test was conducted to assess small study effects. Statistical significance for the overall effect was considered when the 2-tailed P value was less than .05.

    Results: A total of 46 randomized controlled trials, involving 3727 patients, were included in this meta-analysis. The results indicated that WEDS-supported PA programs significantly improved objectively measured moderate-to-vigorous-intensity physical activity (MVPA; SMD 0.66, 95% CI 0.47-0.86, P<.001, I2=69%), subjectively reported PA (SMD 0.5, 95% CI 0.23-0.77, P<.001, I2=79%), steps per day (SMD 0.5, 95% CI 0.23-0.77, P=.009, I2=79%), and QoL (SMD 0.19, 95% CI 0.08-0.31, P<.001, I2=33%) among cancer survivors. Subgroup analysis revealed that interventions incorporating multipartnering tools (no fewer than 2 formats) were effective in improving subjectively reported PA, steps per day, and QoL. Long-term interventions (≥12 weeks) improved objectively measured MVPA, subjectively reported PA, steps per day, and QoL. Interventions tailored to specific cancer types significantly improved steps per day (SMD 0.59, 95% CI 0.1-1.08, P=.008, I2=83%) and QoL (SMD 0.14, 95% CI 0.04-0.23, P=.006, I2=0%).

    Conclusions: We observed that WEDS-supported PA programs are effective in improving the level of PA (both objectively and subjectively), steps per day, and QoL among cancer survivors, but showed no significant effects on sedentary behavior or BMI. In the future, the use of multipartnering tools, appropriate intervention duration, and tailored PA programs should be carefully considered when developing WEDS-supported PA interventions. Further promotion and refinement of WEDS-supported PA programs are warranted.

    Trial Registration: PROSPERO CRD42024582905; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024582905

    J Med Internet Res 2025;27:e74347

    doi:10.2196/74347

    Keywords

    oncology; digital health; physical activity; quality of life; PRISMA 


    Owing to advancements in early detection and breakthroughs in cancer treatment, the number of long-term cancer survivors has significantly increased, transforming cancer into a chronic condition and making it a growing global public health concern [1]. When cancer is considered a chronic disease, it is essential to support cancer survivors’ self-management and enhance their quality of life (QoL). However, cancer survivors often experience various symptom burdens, including reduced cardiorespiratory fitness and muscle strength, fatigue, sleep disturbances, and emotional distress, all of which affect their QoL and mortality [1-8]. These symptom burdens and decreased QoL are even associated with patient survival [9-12]. Maintaining physical activity (PA) has emerged as a promising lifestyle approach for cancer survivors. PA refers to any bodily movement produced by skeletal muscles that requires energy expenditure [13]. For cancer survivors, adequate PA, especially moderate-to-vigorous-intensity PA (MVPA), can improve their cardiorespiratory and muscular fitness, alleviate symptom burden (such as cancer-related fatigue), and enhance QoL [14]. However, when cancer survivors are diagnosed with cancer, their engagement in even little or leisure PA significantly decreases, whereas their time spent sitting increases, which negatively impacts their survival [15,16]. Barriers that prevent cancer survivors from participating in PA may include their symptom burden (such as pain, fatigue, and lymphedema), social factors (such as lack of time, motivation, and support from health care professionals [HCPs]), and lack of information (such as recommendations on PA) [17]. Thus, it is essential to identify interventions that can encourage or remind cancer survivors to increase their level of PA and provide support to enhance their physical fitness, such as muscular fitness and cardiorespiratory fitness, ultimately improving their QoL and survival.

    Digital health, defined as the use of “digital technologies for health” [18], including mobile health (mHealth) apps, electronic health records, electronic medical records, wearable electronic devices (WEDs), telehealth and telemedicine, and personalized medicine [19], is an influential force in the progression of global health care toward improved accessibility and quality [20]. WEDs refer to any kind of electronic device designed to be worn on the user’s body, as either an accessory or an implant [21]. In the context of behavior change techniques [22], interventions supported by WEDs have become increasingly prevalent among cancer survivors to increase their PA by collecting physical and physiological information, enabling continuous real-time self-health surveillance, and providing stimuli for behavior change [23-26]. Moreover, WEDs can be combined with partnering tools to form a wearable electronic device system (WEDS), where partnering tools may include telephone calls, SMS text messages, apps, or websites. Interventions supported by WEDS are effective intervention modalities and can offer an optional and novel approach to promoting PA among cancer survivors [25,27,28].

    In WEDS-supported PA programs, HCPs usually set PA goals for participants, WEDs facilitate self-monitoring and data collection, and partnering tools typically enable patient contact with HCPs or provide timely feedback from HCPs (such as consultation, goal resetting, and guidance) [28]. Owing to these benefits and their portability, studies exploring WEDS-supported PA in oncology rehabilitation have surged dramatically over the past decade [23,29,30]. Positive effects have been observed in improving PA among older adults, adults, and patients with diabetes, cardiovascular-related diseases, and chronic obstructive pulmonary disease [31-35]. Although evidence suggests that WEDS-supported PA programs can benefit patients with cancer by increasing PA levels and improving health-related outcomes (such as fatigue, muscular fitness, aerobic fitness, and QoL), findings from existing studies on the effects of these programs remain inconsistent [8,23,29,30]. For example, Singh et al [23] reported that WEDS-supported PA programs led to a statistically significant increase in patients’ daily step counts, whereas Teo et al [30] found no statistically significant difference in daily step counts between the experimental and control groups. Additionally, in other forms of eHealth-supported interventions for cancer survivors, effectiveness varies depending on the type of eHealth and the duration of the intervention. For example, both Li et al [36] and Su et al [37] reported that the effectiveness of internet-based digital health interventions differed across different subgroups based on the format or duration of the intervention. Moreover, previous studies have consistently overlooked the role of partnering tools, and no researcher has investigated which types of partnering tools may better integrate with WEDs to enhance their effectiveness. Additionally, although numerous studies on this topic have been published, most have focused only on specific types of cancer or examined a limited range of health-related outcomes [29,30]. Furthermore, prior research in this area has predominantly focused on examining the feasibility, acceptability, or overall effects of WED-supported PA programs on cancer survivors. To our knowledge, no meta-analysis has focused specifically on WEDS-supported PA programs, nor has any examined subgroup effects on diverse outcomes, such as different formats of partnering tools for improving PA levels, BMI, or QoL, and decreasing sedentary behavior.

    Thus, the objectives of this meta-analysis are (1) to evaluate the effects of WEDS-supported PA programs on increasing PA-related outcomes and QoL, and (2) to explore which type of WEDS is most effective and the optimal duration of the intervention for cancer survivors.


    Study Design

    This meta-analysis was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement [38] and has been duly registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD42024582905). As the data utilized in this research were exclusively sourced from previously published studies, ethical approval and informed consent were not required.

    Search Strategy

    After consulting a professor of statistics, a comprehensive literature search was carried out, covering the period from the inception of the databases to July 31, 2024. The search was conducted across PubMed, Embase, Web of Science, CENTRAL, and MEDLINE, with access to the full text of the articles. The development of the search strategy was guided by the PICOS (Participants, Interventions, Comparisons, Outcomes, and Study Design) framework, along with the guidelines provided by the Cochrane Collaboration to ensure the integrity of the analysis. This strategy incorporated the use of MeSH (Medical Subject Headings) terms, textual keyword searches, and Boolean logic operations, supplemented by keywords from titles or abstracts, including terms such as neoplasms, carcinomas, tumors, cancer, caregivers, PA trackers, wearable, telemedicine, and telerehabilitation. All search strategies used are presented in Multimedia Appendix 1. The search was limited to studies involving humans and randomized controlled trials and was conducted in English. Moreover, we conducted a rigorous manual review of the bibliographies of the retrieved articles to identify and obtain supplementary relevant scholarly works, thereby enhancing the depth of our analytical inquiry.

    Study Eligibility Criteria

    This meta-analysis considered studies for inclusion based on the following criteria: (1) participants were survivors of any type of cancer, regardless of sex or cancer stage; (2) patients in the intervention group received WEDS-supported PA programs, which included reminders to change behavior, consultations with HCPs, or social support from other patients; (3) patients in the control group received usual care or were placed on a waitlist; (4) the outcomes included at least one of the following indices—objectively measured MVPA, subjectively reported PA, sedentary behavior, QoL, or BMI—without restrictions on the measures used; (5) the publications were written in English; and (6) the studies were designed as randomized controlled trials. Studies were excluded if they were only registered but not yet conducted or if relevant data were incomplete.

    Study Selection and Data Extraction

    The reference management software EndNote X9 (Clarivate Plc) was used to import and screen the titles and abstracts of the studies. Duplications were first removed automatically by EndNote and then meticulously screened by researchers. To ensure alignment with the inclusion criteria, 2 independent authors (ZW and YL) concurrently conducted a thorough screening of the titles and abstracts. Subsequently, they carefully evaluated the full texts of the papers based on the predetermined eligibility criteria. Any discrepancies in the screening process were resolved through discussion or by consulting a third author (QW). Data extraction from the included studies was performed independently by 2 authors (ZW and YL), who meticulously recorded the information using a predefined data extraction template. This template encompassed a range of details, including the first author’s name, year of publication, country where the study was conducted, participants’ ages (mean and SD), sample size, type of cancer diagnosed, types of WEDs and associated tools used, intervention content, duration of the interventions, outcome measures employed, and timing of assessments.

    Quality Assessment

    The methodological quality and risk of bias in the included studies were meticulously assessed by 2 independent reviewers, using the Risk of Bias Tool 2, version 5.1.0. A total of 7 domains were evaluated: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other potential sources of bias. Each domain was graded as “low risk” of bias, “high risk” of bias, and “unclear risk” of bias. The official Cochrane Excel tool was used to automatically compute the overall risk. Disagreements were resolved through discussion or, when necessary, by consulting a third author.

    Data Synthesis and Analysis

    Utilizing R Studio (R Foundation), we conducted heterogeneity evaluations and performed the meta-analysis. To quantify the intervention effects, we computed the standard mean difference (SMD) along with its corresponding 95% CI, and presented the results using forest plots. To obtain more robust results, all data were pooled and analyzed using a random-effects model, while a fixed-effects model was applied when the number of included studies was small (no more than 5) [39]. In cases where a multiarm trial was included, the shared group was divided into subgroups of approximately equal size, 1 for each experimental group [40,41]. In addition, we assessed statistical heterogeneity across all included studies using the I2 statistic and P value. When there were 10 or more studies, the Egger test was conducted to assess small-study effects, with a P value below .05 indicating the possible presence of such effects [42]. To evaluate the robustness and reliability of the pooled results, a sensitivity analysis was performed, using the one-study-out method. Statistical significance for the overall effect was established when the 2-tailed P value was less than .05.


    Search Results and Selection

    The initial search across 5 electronic databases identified 4555 articles. After 1194 duplicates were removed both automatically and manually, 3361 articles were excluded based on their titles and abstracts. Following this initial screening, the full texts of the remaining 153 articles were retrieved, resulting in a final total of 46 studies included in the meta-analysis. The procedures for search and selection are delineated in Figure 1.

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of the study selection process.

    Description of Included Studies

    Study Characteristics

    The attributes of the 46 studies included in this analysis are presented in Multimedia Appendix 2. All studies, conducted as randomized controlled trials, were published between 2005 and 2024 across 8 countries: the United States of America (31 studies) [43-73], Canada (1 study) [74], the United Kingdom (2 studies) [75,76], New Zealand (1 study) [77], Australia (6 studies) [78-83], the Netherlands (2 studies) [84,85], Korea (2 studies) [86,87] and China (1 study) [88].

    Characteristics of Cancer Survivors

    A total of 3727 cancer survivors were enrolled in the studies, with the number of participants ranging from 11 [65] to 412 [85]. The mean age of the included cancer survivors ranged from 12.7 (SD 7.87) years [53] to 73.79 (SD 7.74) years [65]. Regarding cancer types, 19 studies enrolled participants diagnosed with nonspecific types of cancer [45-47,53,54,56,59,67,72,73,75-80,82,85,88], while 27 focused solely on a single cancer type, including breast cancer (15 studies) [43,44,51,52,57,58,60,61,63,64,66,71,74,81,83], colorectal cancer (6 studies) [48,49,62,68,69,86], prostate cancer (4 studies) [55,65,70,87], leukemia or lymphoma (1 study) [50], and glioma (1 study) [84].

    Characteristics of WEDS-Supported PA Programs

    In the included studies, the intervention duration ranged from 4 weeks [45] to 48 weeks [51], with an average duration of 13.4 weeks. The WEDS-supported PA programs consisted of 2 components: WEDs and partnering tools.

    WEDs play a role in step counting, reminders, and data storage. The WEDs used in these studies included pedometers (n=13) [43,56,57,61-63,65,66,75,77,79,85,86], smartwatches (n=4) [64,74,83,84], breath monitors (n=1) [45], smart bands (n=25) [44,46-52,54,55,58,59,67-73,76,78,80-82,87], intelligent sports bracelets (n=1) [88], headbands (n=1) [60], and activity monitors, with no mention of the specific type (n=1) [53]. There are some similarities between smart bands, intelligent sports bracelets, and smartwatches; however, intelligent sports bracelets are considered more fashionable due to their appearance resembling a traditional bracelet, while smart bands are slimmer and simpler in design, focusing primarily on fitness tracking and health monitoring [89]. In comparison, smartwatches have a watch-like form and offer more versatile functionalities, including apps and notifications [90].

    Partnering tools in WEDS differ in their functions, including reminders, consultation, education, and data transmission for researchers. The types of partnering tools used included websites/web pages (n=5) [51,53,79,80,85], apps (n=7) [45,50,58,60,64,66,87], telephone calls (n=13) [43,56,57,61-63,65,71,74,75,77,83,88], SMS text messages (n=2) [48,76], or their combinations (n=19) [44,46,47,49,52,54,55,59,67-70,72,73,78,81,82,84,86].

    In the 46 included articles, the behavior change techniques used in the interventions included goal setting, self-monitoring, feedback and monitoring, and social support. All interventions used goal setting, self-monitoring, and feedback and monitoring, while 8 studies [44,48,51,55,60,65,68,73] incorporated social support.

    Characteristics of the Controls

    Most of the patients in the control groups received usual care (n=35), which included education from HCPs (n=33) or only access to websites or an app without reminders (n=2) [43,44,47-50,53-56,58-70,72,75,77,78,80-82,85-88]. Others were placed on a waiting list (n=11) to receive the respective interventions after the trials [45,46,51,52,57,71,73,74,79,83,84].

    Outcome Measures

    Outcome measures encompassed a diverse array, with assessments conducted at varying intervals and across different follow-up periods for participants.

    Objectively Measured Moderate-to-Vigorous-Intensity Physical Activity

    Researchers in 20 studies assessed objectively measured MVPA using an ActiGraph accelerometer [44,46,47,50,52-55,58,59,64,67-69,71,72,78,80-82,85]. Anderson et al [75] used a SenseWear PA monitor to assess patients’ objectively measured PA, while Ferrante et al [51] used a Fitbit to evaluate patients’ objectively measured PA.

    Steps Per Day

    Researchers in 11 studies assessed steps per day using an ActiGraph accelerometer [44,46,47,50,55,57,64,67,68,72,81]. Anderson et al [75] used a SenseWear PA monitor to assess steps per day, while Ferrante et al [51] and Walsh et al [76] used Fitbit devices. In addition, a pedometer was used to evaluate steps per day by Sajid et al [65] and Frensham et al [79].

    Sedentary Behavior
    Overview

    Researchers in 13 studies used ActiGraph accelerometers to assess sedentary behavior [46,50,54,58,59,64,67,71,81,82].

    Subjectively Measured Physical Activity

    Six scales were used in 17 studies to assess cancer survivors’ subjectively measured PA: the International Physical Activity Questionnaire Short Form [75], the Community Healthy Activities Model Program for Seniors [49,57,62,77], the International Physical Activity Questionnaire [66,84,87], the Short Questionnaire to Assess Health-Enhancing Physical Activity [85], the Godin Leisure-Time Exercise Questionnaire [67,76,86], and the Seven-Day Physical Activity Recall [43,56,61-63].

    Quality of Life

    Nine scales were used to assess the QoL of cancer survivors in 22 studies: the Patient-Reported Outcome Measurement Information System [50], the 36-item Short Form Health Survey—Physical Component [46,61,73,77,79], the Quality of Life in Adult Cancer Survivors [51], the EORTC QLG Core Questionnaire-30 [56,60,66,85,87], the Functional Assessment of Cancer Therapy—General [48,88], the Functional Assessment of Cancer Therapy—Breast [44,58,74], the Functional Assessment of Cancer Therapy—Colorectal [62,86], the RAND-36 Measure of Health-Related Quality of Life [76], and the Pediatric Quality of Life Inventory [53,59].

    Feasibility

    Researchers in 12 studies reported feasibility, which was assessed by retention rate (n=8), wearing time (n=2), whether steps per day improved or not (n=1), and adherence to interventions (n=1) [39-41,44,45,48,49,59,62,63,67,69,77,78].

    Risk of Bias

    Utilizing the revised Cochrane risk-of-bias tool, the 24 studies that utilized intention-to-treat analysis within the inclusion criteria were classified as follows: 6 (25%) studies [44,51,55,77,78,84] were deemed to have a low risk of bias, while 18 (75%) studies [43,46,48,54,57,59,61,62,67,70,71,74,76,80,82,85,86,88] were identified as having some concerns regarding bias. Furthermore, among the studies that used per-protocol analysis (N=22), 6 (27%) studies [47,49,64,75,79,81] were assessed as having a low risk of bias, whereas 15 (68%) studies [50,52,53,56,58,60,63,65,66,68,69,72,73,83,87] were classified as having some concerns, and 1 (5%) study [45] was classified as having a high risk of bias. Concerns regarding risk of bias emerged due to the randomization process (33/47 studies) [43,46,48,50,52-54,56-63,65-74,76,80,82,83,85-88] and the measurement of the outcome (1 of 47 studies) [52]. A high risk of bias was associated with deviation from the intended interventions (1 of 47 studies) [45]. The assessments of risk of bias are comprehensively presented in Figure 2. In addition, the results of the Egger test revealed no evidence of small study effects (objectively measured MVPA: P=.26; subjectively reported PA: P=.09; steps per day: P=.12; sedentary behavior: P=.15; BMI: P=.13; QoL: P=.24; Multimedia Appendix 3).

    ‎
    Figure 2. Results of the assessments of the risk of bias.

    Meta-Analysis Results

    The summary of all outcomes included in this meta-analysis is detailed in Multimedia Appendix 3.

    Primary Outcome: Objectively Measured MVPA
    Total Effects of WEDS-Supported PA Programs

    Investigators from 23 studies, encompassing a total of 1853 participants, quantified the influence of WEDS-supported PA programs on the objectively reported MVPA among cancer survivors. The random-effects model used for pooling the data yielded a significant improvement in the intervention groups (SMD 0.66, 95% CI 0.47-0.86, P<.001, I2=69%; Figure 3). Additionally, the meta-analysis results remained stable after the omission of individual studies (Multimedia Appendix 4).

    ‎
    Figure 3. Total effects on objectively measured moderate-to-vigorous-intensity physical activity [43,46,48,49,51-54,57,58,62,63,66-68,70,71,78-81,84]. SMD: standardized mean difference.
    Subgroup Analysis

    Studies grouped by the use of multipartnering tools suggested that WEDS-supported PA programs, whether with both multipartnering tools (SMD 0.68, 95% CI 0.44-0.92, P<.001, I2=70%) or without (SMD 0.63, 95% CI 0.26-1.01, P<.001, I2=71%), showed significant improvements in objectively measured MVPA (Figure 4).

    Upon categorizing the studies based on the duration of the intervention, the pooled results indicated that WEDS-supported PA programs with long-term durations (≥12 weeks) were effective in increasing objectively measured MVPA (SMD 0.72, 95% CI 0.53-0.92, P<.001, I2=67%; Figure 5). When grouped by whether the intervention was designed for a specific cancer type, both the “yes” group (SMD 0.74, 95% CI 0.52-0.96, P<.001, I2=64%) and the “no” group (SMD 0.37, 95% CI 0.04-0.7, P=.02, I2=52%) showed significant differences (Figure 6). Heterogeneity in these 2 subgroups showed a modest to notable decrease (Multimedia Appendix 2). Duration and whether the intervention was designed for patients with a specific cancer type may be sources of heterogeneity.

    ‎
    Figure 4. Subgroup analysis on objectively measured moderate-to-vigorous-intensity physical activity, grouped by the use of multipartnering tools [43,46,48,49,51-54,57,58,62,63,66-68,70,71,78-81,84]. SMD: standardized mean difference.
    ‎
    Figure 5. Subgroup analysis on objectively measured moderate-to-vigorous-intensity physical activity, grouped by intervention duration [43,46,48,49,51-54,57,58,62,63,66-68,70,71,78-81,84]. SMD: standardized mean difference.
    ‎
    Figure 6. Subgroup analysis on objectively measured moderate-to-vigorous-intensity physical activity, grouped by whether the intervention was designed for a specific cancer type [43,46,48,49,51-54,57,58,62,63,66-68,70,71,78-81,84]. SMD: standardized mean difference.

    Secondary Outcomes: Subjectively Reported PA

    Total Effects of WEDS-Supported PA Programs

    Data gathered from 15 studies, involving a total of 2016 participants, were used to assess the efficacy of WEDS-supported PA programs in increasing subjectively reported PA. The results of the random-effects model suggested a significant improvement in the experimental groups (SMD 0.5, 95% CI 0.23-0.77, P<.001, I2=79%; Figure 7). Furthermore, utilizing the one-study-out approach for sensitivity analysis, the findings remained stable (Multimedia Appendix 4).

    ‎
    Figure 7. Total effects on subjectively reported physical activity [42,48,55,56,60-62,65,66,75,76,83-86]. SMD: standardized mean difference.
    Subgroup Analysis

    In the pooled analysis based on the use of multipartnering tools, the results in the subgroup without multipartnering tools (SMD 0.39, 95% CI 0.17-0.61, P<.001, I2=62%) showed a significant improvement in subjectively reported PA (Figure 8).

    Upon categorizing the studies based on the duration of intervention, subjectively reported PA significantly increased in the long-term intervention groups (no less than 12 weeks; SMD 0.52, 95% CI 0.24-0.81, P<.001, I2=80%; Figure 9). When grouped by whether the intervention was designed for a specific cancer type, patients’ subjectively reported PA improved in both the “yes” group (SMD 0.56, 95% CI 0.23-0.89, P<.001, I2=82%) and the “no” group (SMD 0.25, 95% CI 0.04-0.06, P=.02, I2=0%; Figure 10). The use of multipartnering tools and whether interventions were designed for patients with specific cancer types may be sources of heterogeneity (Multimedia Appendix 2).

    ‎
    Figure 8. Subgroup analysis on subjectively reported physical activity, grouped by the use of multipartnering tools [42,48,55,56,60-62,65,66,75,76,83-86]. SMD: standardized mean difference.
    ‎
    Figure 9. Subgroup analysis on subjectively reported physical activity, grouped by intervention duration [42,48,55,56,60-62,65,66,75,76,83-86]. SMD: standardized mean difference.
    ‎
    Figure 10. Subgroup analysis on subjectively reported physical activity, grouped by whether the intervention was designed for a specific cancer type [42,48,55,56,60-62,65,66,75,76,83-86]. SMD: standardized mean difference.

    Secondary Outcomes: Steps Per Day

    Total Effects of WEDS-Supported PA Programs

    Fifteen studies assessed the impact of WEDS-supported PA programs on the steps per day of patients with cancer [44,46,47,50,55,57,64,65,67,68,72,75,76,79,81]. The random-effects model revealed a significant difference between the intervention and control groups (SMD 0.54, 95% CI 0.14-0.94, P=.002, I2=81%; Figure 11). Utilizing the one-study-out approach for sensitivity analysis, the pooled findings remained robust upon the sequential exclusion of individual studies (Multimedia Appendix 4).

    ‎
    Figure 11. Total effects on steps per day [45,46,49,50,54,56,63,64,66-68,71,74,75,78,80]. SMD: standardized mean difference.
    Subgroup Analysis

    In the pooled results of the partnering tools used in WEDS-supported PA programs, compared with the subgroup without multipartnering tools, the subgroup using multipartnering showed a significant difference in the number of steps per day (SMD 0.59, 95% CI 0.07-1.1, P=.006, I2=85%; Figure 12). Heterogeneity decreased in the subgroup without multipartnering tools (I2=46%).

    The pooled findings from the subgroup analysis indicated a significant increase in steps per day when the duration of WEDS-supported PA programs was no less than 12 weeks (SMD 0.55, 95% CI 0.11-0.99, P=.003 I2=83%; Figure 13). Heterogeneity decreased in the group with a duration of less than 12 weeks (I2=27%).

    When grouped by whether the intervention was designed for a specific cancer type, patients’ steps per day improved in the “yes” group (SMD 0.59, 95% CI 0.1-1.08, P=.008, I2=83%; Figure 14). Heterogeneity decreased in the group without specific cancer types (I2=43%).

    The use of multipartnering tools, intervention duration, and whether patients were allocated based on specific cancer types may be sources of heterogeneity.

    ‎
    Figure 12. Subgroup analysis on steps per day, grouped by the use of multipartnering tools [45,46,49,50,54,56,63,64,66-68,71,74,75,78,80]. SMD: standardized mean difference.
    ‎
    Figure 13. .Subgroup analysis on steps per day, grouped by intervention duration [45,46,49,50,54,56,63,64,66-68,71,74,75,78,80]. SMD: standardized mean difference.
    ‎
    Figure 14. Subgroup analysis on steps per day, grouped by whether the intervention was designed for a specific cancer type [45,46,49,50,54,56,63,64,66-68,71,74,75,78,80]. SMD: standardized mean difference.

    Secondary Outcomes: Sedentary Behavior

    Total Effects of WEDS-Supported PA Programs

    Data gathered from 13 studies, involving a total of 912 participants, were used to assess the efficacy of WEDS-supported PA programs in decreasing sedentary behavior. The results of the random-effects model demonstrated that WEDS-supported PA programs did not significantly decrease cancer survivors’ sedentary behavior (SMD −0.63, 95% CI −1.34 to 0.07, P=.08, I2=92%; Figure 15). Utilizing the one-study-out approach for sensitivity analysis, the findings remained robust (Multimedia Appendix 4).

    ‎
    Figure 15. Total effects on sedentary behavior [43,45,49,53,57,58,63,66,70,71,79-81]. SMD: standardized mean difference.
    Subgroup Analysis

    In the pooled results for the subgroups of WEDS-supported PA programs, usage of multipartnering tools, durations of interventions, and whether interventions were designed for specific cancer types, no significant differences were observed (Figures 16-18 and Multimedia Appendix 2).

    Heterogeneity decreased in the group with a duration of less than 12 weeks (I2=0%).

    ‎
    Figure 16. Subgroup analysis on sedentary behaviors, grouped by the use of multipartnering tools [43,45,49,53,57,58,63,66,70,71,79-81]. SMD: standardized mean difference.
    ‎
    Figure 17. Subgroup analysis on sedentary behaviors, grouped by intervention duration [43,45,49,53,57,58,63,66,70,71,79-81]. SMD: standardized mean difference.
    ‎
    Figure 18. Subgroup analysis on sedentary behaviors, grouped by whether the intervention was designed for a specific cancer type [43,45,49,53,57,58,63,66,70,71,79-81]. SMD: standardized mean difference.

    Secondary Outcomes: BMI

    Total Effects of WEDS-Supported PA Programs on BMI

    Data gathered from 12 studies, involving a total of 1134 participants, were used to assess the efficacy of WEDS-supported PA programs in decreasing BMI. The results of the random-effects model demonstrated no significant difference between the experimental and control groups (SMD −0.07, 95% CI −0.18 to 0.05, P=.27, I2=0%; Figure 19). In addition, the pooled findings remained robust upon the sequential exclusion of individual studies (Multimedia Appendix 4).

    ‎
    Figure 19. Total effects on BMI [42,50,51,57,65,74,75,77,79,81,83,86]. SMD: standardized mean difference.
    Subgroup Analysis (BMI)

    In the pooled analysis of subgroups within WEDS-supported PA programs, considering factors such as the use of multipartnering tools, the duration of interventions, and whether the interventions were tailored to specific cancer types, no significant differences were found (Figures 20 and 21 and Multimedia Appendix 2).

    ‎
    Figure 20. Subgroup analysis on BMI, grouped by the use of multipartnering tools [42,50,51,57,65,74,75,77,79,81,83,86]. SMD: standardized mean difference.
    ‎
    Figure 21. Subgroup analysis on BMI, grouped by whether the intervention was designed for a specific cancer type [42,50,51,57,65,74,75,77,79,81,83,86]. SMD: standardized mean difference.

    Secondary Outcomes: Quality of Life

    Total Effects of WEDS-Supported PA Programs

    Researchers from 21 studies assessed the effectiveness of WEDS-supported PA programs on the QoL of cancer survivors. The random-effects model revealed a significant difference between the experimental and control groups in the pooled results (SMD 0.19, 95% CI 0.08-0.31, P<.001, I2=33%; Figure 22). A sensitivity analysis was conducted using the one-study-out method, and the results remained robust.

    ‎
    Figure 22. Total effects on quality of life. SMD: standardized mean difference. [36,44,47,49,50,52,55,58-61,65,72,73,75-78,84-86]. SMD: standardized mean difference.
    Subgroup Analysis (QoL)

    In the pooled results based on the usage of multipartnering tools, both the subgroup using multipartnering tools (SMD 0.35, 95% CI 0.05-0.65, P<.001, I2=56%) and the subgroup not using them (SMD 0.12, 95% CI 0.03-0.21, P=.02, I2=0%) showed significant improvement in QoL (Figure 23). Notably, heterogeneity sharply decreased in the noncombination group (I2=0%).

    The pooled findings from the subgroup analysis indicated a significant increase in QoL when the duration of WEDS-supported PA programs was no less than 12 weeks, accompanied by a sharp decrease in heterogeneity (SMD 0.12, 95% CI 0.04-0.21, P<.001, I2=0%; Figure 24).

    When grouped by whether the intervention was designed for a specific cancer type, patients’ QoL improved in the specific cancer type group (SMD 0.14, 95% CI 0.04-0.23, P=.006, I2=0%; Figure 25), with a sharp decrease in heterogeneity. Whether interventions were designed for a specific cancer type, whether multipartnering tools were used, and the duration of interventions might be potential sources of heterogeneity.

    ‎
    Figure 23. Subgroup analysis on quality of life, grouped by the use of multipartnering tools [36,44,47,49,50,52,55,58-61,65,72,73,75-78,84-86]. SMD: standardized mean difference.
    ‎
    Figure 24. Subgroup analysis on quality of life, grouped by intervention duration [36,44,47,49,50,52,55,58-61,65,72,73,75-78,84-86]. SMD: standardized mean difference.
    ‎
    Figure 25. Subgroup analysis on quality of life, grouped by whether the intervention was designed for a specific cancer type [36,44,47,49,50,52,55,58-61,65,72,73,75-78,84-86]. SMD: standardized mean difference.

    Principal Findings

    A total of 46 studies met the eligibility criteria for this meta-analysis. Compared with usual care or waitlists, WEDS-supported PA programs significantly improved cancer survivors’ objectively measured MVPA, subjectively measured PA, steps per day, and QoL, but showed no significant effect on reducing sedentary behavior or improving BMI. The SMD of total effects ranged from −0.07 to 0.66, which is consistent with previous studies [23,29], thereby confirming the effectiveness of WEDS-supported PA programs.

    Quality of Evidence and Methodology

    Overall, the evidence quality and methodology were rated as moderate; 24 studies used the intention-to-treat analysis, while 22 utilized per-protocol analysis. Using the revised Cochrane Risk-of-Bias tool, it was determined that 12 of 46 (26%) studies were assessed as having a low risk of bias, 33 of 46 (72%) studies raised some concerns, and only 1 of 46 (2%) studies was deemed to have a high risk of bias. Specifically, of these 46 studies, 33 (72%) were flagged for potential bias related to the randomization process, and 1 (2%) raised concerns regarding bias in the measurement of outcomes. In detail, all studies were assessed as having either a low or unclear risk associated with the randomization process, primarily because some investigators failed to provide comprehensive details on the randomization techniques or adequately describe the allocation concealment methods. Consequently, the overall methodological quality was deemed moderate. These findings underscore the need for additional randomized controlled trials in the future, with a focus on more transparent reporting to enhance the robustness of research findings.

    PA-Related Outcomes

    The results of this meta-analysis demonstrated that WEDS-supported PA programs significantly improved objectively measured MVPA, subjectively reported PA, steps per day, and QoL in cancer survivors, but had no significant effect on sedentary behavior or BMI. The findings related to objectively measured MVPA, subjectively reported PA, and steps per day are consistent with those of previous studies [23].

    For cancer survivors, these interventions serve as tracking devices (continuously collecting current activity), feedback tools (providing immediate information on activity levels), and environmental cues (reminders to be active). Through these continuous influences, patients’ levels of PA increase [91]. In addition, the partnering tools used in these interventions enable cancer survivors to record, report, and contact HCPs anytime and anywhere they need, ensuring timely revision of exercise prescriptions and providing knowledge related to their disease and symptoms [78,80]. Moreover, the similar results between subjectively reported PA and objectively measured MVPA highlight the feasibility of WEDS-supported PA programs in improving PA among cancer survivors. On the contrary, we found that sedentary behavior did not improve significantly. This may be because patients may choose to ignore activity alarms while remaining sedentary [46]. More cognitive behavioral therapy is needed to enhance patients’ awareness and motivation to reduce sedentary behavior [92]. Regarding BMI, the lack of a significant difference might be due to patients gaining muscle mass, which can offset weight loss, resulting in no apparent change in BMI despite positive effects on fitness [93]. Furthermore, diet management has been shown to play a more significant role in weight loss programs than PA alone in many studies [94,95].

    The results of the subgroup analysis on the usage of multipartnering tools revealed that objectively measured MVPA significantly improved regardless of whether multipartnering tools were used. This outcome may be because, at the beginning of the interventions, researchers set PA goals for participants and adjusted those goals based on their performance through the partnering tools. Whether or not multipartnering tools were used, the partnering tools could still serve as reminders for patients to complete more MVPA. Thus, the use of multipartnering tools may not have influenced objectively measured MVPA. By contrast, for subjectively reported PA, a significant difference was observed in the nonusage of multipartnering tools. When patients are assisted by multipartnering tools to remind them to improve PA, they may lack initiative, and their self-efficacy regarding PA may decrease, along with their perception of subjectively reported PA [96]. For steps per day, the multipartnering tools groups showed significant differences. This may be because various partnering tools play a more comprehensive role, such as providing real-time conversations through telephone calls and offering relevant knowledge via apps or websites. Through this type of multimedia stimulation, participants can receive more comprehensive reminders and encouragement to remain physically active.

    Moreover, we observed significant improvements in objectively measured MVPA, subjectively reported PA, steps per day, and QoL among participants who received long-term (no less than 12 weeks) interventions, which is consistent with previous similar findings [36]. Research has shown that longer-term interventions are conducive to forming healthier lifestyles and developing lasting habits. Therefore, the patients can derive enjoyment from PA and are more willing to complete additional PA programs [97,98]. Similar to the overall effects, the results showed no significant improvements in sedentary behavior or BMI in the subgroups categorized by duration.

    When studies were grouped by whether the intervention was designed for a specific cancer type, the results showed that both steps per day and QoL significantly improved in patients who received interventions tailored to their specific cancer type. This may be because tailored programs could better meet patients’ specific needs, such as providing information related to their cancer type and offering PA programs suited to their condition. By contrast, regardless of whether the interventions were tailored or not, the effectiveness of WEDS-supported PA programs in improving objectively measured MVPA, subjectively reported PA, sedentary behavior, and BMI did not change.

    In brief, WEDS is promising for improving MVPA, subjectively reported PA, steps per day, and QoL. Long-term interventions (≥12 weeks) are effective in improving PA-related outcomes, except for sedentary behavior, and the use of multipartnering tools should depend on the patients’ preferences and habits. Attention should also be given to the proper use of multipartnering tools, the optimal duration, and whether the intervention is tailored to specific cancer types when developing new WEDS-supported interventions. Further studies are needed to explore the most effective intervention characteristics for improving patients’ sedentary behavior and BMI. This approach will facilitate the development of more effective WEDS-supported interventions.

    Quality of Life

    This meta-analysis demonstrated that, compared with usual care or waitlists, WEDS-supported PA programs have a significant effect on the QoL of cancer survivors, which is consistent with the findings of previous studies [23]. The QoL assessed in the included studies was health-related QoL, which encompasses not only basic physical functioning but also patient participation in activities such as work and entertainment [99]. WEDS-supported PA programs significantly improved cancer survivors’ inactive lifestyles, enhanced their self-efficacy and feelings of self-worth, increased their satisfaction with life, and indirectly influenced their QoL [100]. Moreover, appropriate social relationships, cancer and self-care education, and psychological support provided through partnering tools could further help improve cancer survivors’ QoL [101,102].

    In the subgroup analysis, regarding the use of multipartnering tools, QoL was significantly improved in both the usage and nonusage groups. This may be because interventions in both groups provided reminders to patients, which significantly enhanced participants’ PA levels and indirectly reduced their symptom burden, thereby improving QoL. When grouped by intervention duration, QoL was significantly improved in the long-term subgroups. Longer intervention durations enable patients to develop sustained habits of positive PA. Additionally, patients may have more opportunities to access diverse forms of support over the long term, which provides greater encouragement for engaging in PA and fosters the adoption of self-management strategies, thereby improving their QoL [103]. Furthermore, we observed a significant improvement in QoL among patients who received interventions designed for a specific cancer type. Researchers could tailor specific programs, such as PA regimens and psychological support from HCPs, according to the characteristics of each patient’s cancer.

    In essence, WEDS-supported PA programs enable cancer survivors to engage positively with WEDs and partnering tools, with the potential to reduce negative affective states and consequently enhance their QoL. Researchers should carefully consider the duration of intervention when designing WEDS-supported strategies. Additionally, further investigation is warranted to evaluate the effectiveness of partnering tools in addressing the specific needs of cancer survivors.

    Limitations

    This study has several limitations. First, heterogeneity existed due to variations in the format of partnering tools, durations of intervention, and types of cancer. Second, the reporting of study results may have been influenced by commercial interests associated with PA improvements, posing a potential risk of publication bias. Additionally, a significant proportion of the research was conducted in Western countries, and responses to WEDS-supported PA programs may vary among participants from different regions [104]. Finally, despite conducting an exhaustive literature search, publication bias could not be completely eliminated. Therefore, the outcomes of this meta-analysis should be interpreted with caution, and more high-quality randomized controlled trials are needed in the future.

    Implications

    In this study, we quantitatively integrated existing findings and found that WEDS-supported PA programs were effective in improving PA levels (both objectively and subjectively), daily steps, and QoL. The mechanisms through which WEDS-supported PA programs bring clinical benefits may include providing persistent reminders to encourage PA, offering convenient access to consultations with HCPs, collecting health-related data, recording electronic health records, and facilitating social groups for patients to communicate with others facing similar conditions [47,50]. Thus, HCPs can use WEDS as a supplementary tool to monitor patients’ physiological data, manage care, adjust exercise prescriptions, and provide timely feedback and disease-related information.

    With increasing research focusing on WEDs and other forms of eHealth as interventions to promote PA among cancer survivors, WEDS has the potential to become a valuable tool for HCPs and a novel reminder and management resource for cancer survivors. It can automatically sync data, thereby reducing the self-monitoring burden associated with traditional web-based interventions [105]. Additionally, previous studies often failed to adequately consider the role of partnering tools, resulting in their underutilization and a missed opportunity to maximize the benefits for patients’ PA engagement. Furthermore, we observed that certain aspects of the intervention, such as the use of multipartnering tools, the duration of the intervention, and whether the intervention was tailored for specific cancer types, influenced its overall efficacy. This underscores the need for further standardization and more rigorous quantitative studies to refine the WEDS-supported intervention framework and to fully explore the potential benefits of WEDS-supported PA programs. Moreover, efforts should be made to enable data intercommunication between different commercial WEDs, thereby improving the feasibility and accessibility of these interventions.

    Conclusions

    WEDS-supported PA programs offer a convenient and affordable method for assisting cancer survivors by serving as reminders and records of their PA. This meta-analysis of randomized controlled trials revealed that WEDS-supported PA programs significantly improved cancer survivors’ level of PA (both objectively and subjectively), steps per day, and QoL, but had no significant effect on reducing sedentary behavior or BMI. These results varied based on the use of multipartnering tools, intervention duration, and patients’ cancer type. Further standardization and promotion of WEDS-supported PA programs are warranted in the future.

    Acknowledgments

    This work was supported by the National Natural Science Foundation of China (grant 82172842); the China Medical Board (grant 22-482); the Ministry of Education University-Industry Collaborative Education Program (grant 230720523707281); the Sichuan University Graduate Students Education and Teaching Reform Research Program (grants GSSCU2023090 and GSSCU2023095); and the Chengdu Eastern New Area Municipal Administration Committee Program (grants 200304 and 00402053A29YN).

    Data Availability

    The datasets generated or analyzed during this study are available from the corresponding author (YS) on reasonable request.

    Authors' Contributions

    Conceptualization: YS, ZW, YL

    Data curation: ZW, YL

    Formal analysis: ZW, YL

    Funding acquisition: YS

    Investigation: ZW, YL

    Methodology: YS, ZW, YL, QW

    Project administration: YS

    Resources: YS

    Supervision: YS, QW

    Validation: ZW, YL

    Visualization: ZW, YL

    Writing—original draft: ZW

    Writing—review & editing: YS, ZW, YL, QW

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Search strategies.

    DOCX File, 16 KB
    Multimedia Appendix 2

    Characteristics of included studies.

    DOCX File, 38 KB
    Multimedia Appendix 3

    Summary of all outcomes included in the meta-analysis.

    DOCX File, 32 KB
    Multimedia Appendix 4

    Results of the sensitivity analysis.

    DOCX File, 851 KB
    Checklist 1

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

    PDF File, 175 KB
    1. Firkins J, Hansen L, Driessnack M, Dieckmann N. Quality of life in “chronic” cancer survivors: a meta-analysis. J Cancer Surviv. Aug 2020;14(4):504-517. [CrossRef] [Medline]
    2. Mogavero MP, DelRosso LM, Fanfulla F, Bruni O, Ferri R. Sleep disorders and cancer: state of the art and future perspectives. Sleep Med Rev. Apr 2021;56:101409. [CrossRef] [Medline]
    3. Cancer survivors and physical activity. National Cancer Institute. URL: https://progressreport.cancer.gov/after/physical_activity [Accessed 2025-07-05]
    4. Peel AB, Thomas SM, Dittus K, Jones LW, Lakoski SG. Cardiorespiratory fitness in breast cancer patients: a call for normative values. J Am Heart Assoc. Jan 13, 2014;3(1):e000432. [CrossRef] [Medline]
    5. Bower JE. Cancer-related fatigue--mechanisms, risk factors, and treatments. Nat Rev Clin Oncol. Oct 2014;11(10):597-609. [CrossRef] [Medline]
    6. Mols F, Vingerhoets AJJM, Coebergh JW, van de Poll-Franse LV. Quality of life among long-term breast cancer survivors: a systematic review. Eur J Cancer. Nov 2005;41(17):2613-2619. [CrossRef] [Medline]
    7. Riba MB, Donovan KA, Andersen B, et al. Distress Management, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. Oct 1, 2019;17(10):1229-1249. [CrossRef] [Medline]
    8. Bettariga F, Galvao DA, Taaffe DR, et al. Association of muscle strength and cardiorespiratory fitness with all-cause and cancer-specific mortality in patients diagnosed with cancer: a systematic review with meta-analysis. Br J Sports Med. May 2, 2025;59(10):722-732. [CrossRef] [Medline]
    9. Dohrn IM, Sjöström M, Kwak L, Oja P, Hagströmer M. Accelerometer-measured sedentary time and physical activity—a 15 year follow-up of mortality in a Swedish population-based cohort. J Sci Med Sport. Jul 2018;21(7):702-707. [CrossRef] [Medline]
    10. Ma QQ, Yao Q, Lin L, Chen GC, Yu JB. Sleep duration and total cancer mortality: a meta-analysis of prospective studies. Sleep Med. 2016;27-28:39-44. [CrossRef] [Medline]
    11. Kwok CS, Kontopantelis E, Kuligowski G, et al. Self-reported sleep duration and quality and cardiovascular disease and mortality: a dose-response meta-analysis. J Am Heart Assoc. Aug 7, 2018;7(15):e008552. [CrossRef] [Medline]
    12. Ponizovsky AM, Haklai Z, Goldberger N. Association between psychological distress and mortality: the case of Israel. J Epidemiol Community Health. Aug 2018;72(8):726-732. [CrossRef]
    13. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126-131. [Medline]
    14. Brown JC, Winters‐Stone K, Lee A, Schmitz KH. Cancer, physical activity, and exercise. Compr Physiol. Oct 2012;2(4):2775-2809. [CrossRef]
    15. Cao C, Friedenreich CM, Yang L. Association of daily sitting time and leisure-time physical activity with survival among us cancer survivors. JAMA Oncol. Mar 1, 2022;8(3):395-403. [CrossRef] [Medline]
    16. Gilchrist SC, Howard VJ, Akinyemiju T, et al. Association of sedentary behavior with cancer mortality in middle-aged and older US adults. JAMA Oncol. Aug 1, 2020;6(8):1210-1217. [CrossRef] [Medline]
    17. Browall M, Mijwel S, Rundqvist H, Wengström Y. Physical activity during and after adjuvant treatment for breast cancer: an integrative review of women’s experiences. Integr Cancer Ther. Mar 2018;17(1):16-30. [CrossRef] [Medline]
    18. WHO global strategy on digital health 2020-2025. World Health Organization (WHO). 2021. URL: https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf [Accessed 2025-07-05]
    19. Awati R, Bernstein C. What is digital health (digital healthcare)? TechTarget. URL: https://www.techtarget.com/searchhealthit/definition/digital-health-digital-healthcare [Accessed 2025-07-05]
    20. Jimenez G, Spinazze P, Matchar D, et al. Digital health competencies for primary healthcare professionals: a scoping review. Int J Med Inform. Nov 2020;143:104260. [CrossRef] [Medline]
    21. Hanna KT, Yasar K. What is wearable technology? definition, uses and examples. TechTarget. URL: https://www.techtarget.com/searchmobilecomputing/definition/wearable-technology [Accessed 2025-07-05]
    22. Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. Aug 2013;46(1):81-95. [CrossRef] [Medline]
    23. Singh B, Zopf EM, Howden EJ. Effect and feasibility of wearable physical activity trackers and pedometers for increasing physical activity and improving health outcomes in cancer survivors: a systematic review and meta-analysis. J Sport Health Sci. Mar 2022;11(2):184-193. [CrossRef] [Medline]
    24. Zhou W, Cho Y, Pu J, Shang S. Trends in wearable device use among cancer survivors in the United States from 2019 to 2022. J Geriatr Oncol. May 2024;15(4):101729. [CrossRef] [Medline]
    25. Beauchamp UL, Pappot H, Holländer-Mieritz C. The use of wearables in clinical trials during cancer treatment: systematic review. JMIR Mhealth Uhealth. Nov 11, 2020;8(11):e22006. [CrossRef] [Medline]
    26. Li C, Chen X, Bi X. Wearable activity trackers for promoting physical activity: a systematic meta-analytic review. Int J Med Inform. Aug 2021;152:104487. [CrossRef] [Medline]
    27. Lewis ZH, Lyons EJ, Jarvis JM, Baillargeon J. Using an electronic activity monitor system as an intervention modality: a systematic review. BMC Public Health. Jun 24, 2015;15:585. [CrossRef] [Medline]
    28. Chen X, Kim DH, Lu N. Introduction: wearable devices. Chem Rev. May 22, 2024;124(10):6145-6147. [CrossRef] [Medline]
    29. Pan M, Wu K, Zhao J, Hou X, Chen P, Wang B. Effects of wearable physical activity tracking for breast cancer survivors: a systematic review and meta-analysis. Int J Nurs Knowl. Apr 2024;35(2):117-129. [CrossRef] [Medline]
    30. Teo NR, Siew LED, Ang WHD, Lau Y. Wearable-technology-assisted interventions for breast-cancer survivors: a meta-analysis and meta-regression. Semin Oncol Nurs. Jun 2023;39(3):151403. [CrossRef] [Medline]
    31. Shah AJ, Althobiani MA, Saigal A, Ogbonnaya CE, Hurst JR, Mandal S. Wearable technology interventions in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. NPJ Digit Med. Nov 27, 2023;6(1):222. [CrossRef] [Medline]
    32. Garcia RE, Newman AB, Johnson E, et al. Using wearable devices to examine the associations of sedentary behavior with perceived and performance fatigability among older adults: the Study of Muscle, Mobility and Aging (SOMMA). Sensors (Basel). Apr 25, 2025;25(9):2722. [CrossRef] [Medline]
    33. Ravanelli N, Lefebvre K, Brough A, Paquette S, Lin W. Validation of an open-source smartwatch for continuous monitoring of physical activity and heart rate in adults. Sensors (Basel). 25(9):2926. [CrossRef]
    34. Moayedi Y, Foroutan F, Gao Y, et al. Developments in digital wearable in heart failure and the rationale for the design of TRUE-HF (Ted Rogers Understanding of Exacerbations in Heart Failure) Apple CPET study. Circ Heart Fail. Jun 2025;18(6):e012204. [CrossRef] [Medline]
    35. Leung W, Vo K, Clough M, Frias R. The use of wearable devices on physical activity levels among individuals living with diabetes: 2017 BRFSS. Prim Care Diabetes. Aug 2024;18(4):466-469. [CrossRef] [Medline]
    36. Li J, Zhu C, Liu C, Su Y, Peng X, Hu X. Effectiveness of eHealth interventions for cancer-related pain, fatigue, and sleep disorders in cancer survivors: a systematic review and meta-analysis of randomized controlled trials. J Nurs Scholarsh. Mar 2022;54(2):184-190. [CrossRef] [Medline]
    37. Su CC, Guo SE, Kuo YW. Effects of internet-based digital health interventions on the physical activity and quality of life of colorectal cancer survivors: a systematic review and meta-analysis. Support Care Cancer. Feb 20, 2024;32(3):168. [CrossRef] [Medline]
    38. Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. Mar 29, 2021;372:n160. [CrossRef] [Medline]
    39. Tufanaru C, Munn Z, Stephenson M, Aromataris E. Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness. Int J Evid Based Healthc. 2015;13(3):196-207. [CrossRef]
    40. Rücker G, Cates CJ, Schwarzer G. Methods for including information from multi-arm trials in pairwise meta-analysis. Res Synth Methods. Dec 2017;8(4):392-403. [CrossRef] [Medline]
    41. da Costa BR, Juni P. Systematic reviews and meta-analyses of randomized trials: principles and pitfalls. Eur Heart J. Dec 14, 2014;35(47):3336-3345. [CrossRef] [Medline]
    42. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. Sep 13, 1997;315(7109):629-634. [CrossRef] [Medline]
    43. Pinto BM, Frierson GM, Rabin C, Trunzo JJ, Marcus BH. Home-based physical activity intervention for breast cancer patients. J Clin Oncol. May 20, 2005;23(15):3577-3587. [CrossRef] [Medline]
    44. Phillips SM, Starikovsky J, Solk P, et al. Feasibility and preliminary effects of the Fit2ThriveMB pilot physical activity promotion intervention on physical activity and patient reported outcomes in individuals with metastatic breast cancer. Breast Cancer Res Treat. Nov 2024;208(2):391-403. [CrossRef] [Medline]
    45. Alberts NM, Leisenring WM, Flynn JS, et al. Wearable respiratory monitoring and feedback for chronic pain in adult survivors of childhood cancer: a feasibility randomized controlled trial from the childhood cancer survivor study. JCO Clin Cancer Inform. Nov 2020;4(4):1014-1026. [CrossRef] [Medline]
    46. Blair CK, Harding E, Wiggins C, et al. A home-based mobile health intervention to replace sedentary time with light physical activity in older cancer survivors: randomized controlled pilot trial. JMIR Cancer. Apr 13, 2021;7(2):e18819. [CrossRef] [Medline]
    47. Cadmus-Bertram L, Tevaarwerk AJ, Sesto ME, Gangnon R, Van Remortel B, Date P. Building a physical activity intervention into clinical care for breast and colorectal cancer survivors in Wisconsin: a randomized controlled pilot trial. J Cancer Surviv. Aug 2019;13(4):593-602. [CrossRef] [Medline]
    48. Chan H, Van Loon K, Kenfield SA, et al. Quality of life of colorectal cancer survivors participating in a pilot randomized controlled trial of physical activity trackers and daily text messages. Support Care Cancer. May 2022;30(5):4557-4564. [CrossRef] [Medline]
    49. Chan JM, Van Blarigan EL, Langlais CS, et al. Feasibility and acceptability of a remotely delivered, web-based behavioral intervention for men with prostate cancer: four-arm randomized controlled pilot trial. J Med Internet Res. Dec 31, 2020;22(12):e19238. [CrossRef] [Medline]
    50. Chow EJ, Doody DR, Di C, et al. Feasibility of a behavioral intervention using mobile health applications to reduce cardiovascular risk factors in cancer survivors: a pilot randomized controlled trial. J Cancer Surviv. Aug 2021;15(4):554-563. [CrossRef]
    51. Ferrante JM, Devine KA, Bator A, et al. Feasibility and potential efficacy of commercial mHealth/eHealth tools for weight loss in African American breast cancer survivors: pilot randomized controlled trial. Transl Behav Med. Oct 8, 2020;10(4):938-948. [CrossRef] [Medline]
    52. Hartman SJ, Nelson SH, Myers E, et al. Randomized controlled trial of increasing physical activity on objectively measured and self-reported cognitive functioning among breast cancer survivors: the memory & motion study. Cancer. Jan 1, 2018;124(1):192-202. [CrossRef] [Medline]
    53. Howell CR, Krull KR, Partin RE, et al. Randomized web-based physical activity intervention in adolescent survivors of childhood cancer. Pediatr Blood Cancer. Aug 2018;65(8):e27216. [CrossRef] [Medline]
    54. Johnson AM, Baker KS, Haviland MJ, et al. A pilot randomized controlled trial of a Fitbit- and Facebook-based physical activity intervention for young adult cancer survivors. J Adolesc Young Adult Oncol. Aug 2022;11(4):379-388. [CrossRef] [Medline]
    55. Kenfield SA, Van Blarigan EL, Ameli N, et al. Feasibility, acceptability, and behavioral outcomes from a technology-enhanced behavioral change intervention (Prostate 8): a pilot randomized controlled trial in men with prostate cancer. Eur Urol. Jun 2019;75(6):950-958. [CrossRef] [Medline]
    56. Ligibel JA, Meyerhardt J, Pierce JP, et al. Impact of a telephone-based physical activity intervention upon exercise behaviors and fitness in cancer survivors enrolled in a cooperative group setting. Breast Cancer Res Treat. Feb 2012;132(1):205-213. [CrossRef] [Medline]
    57. Matthews CE, Wilcox S, Hanby CL, et al. Evaluation of a 12-week home-based walking intervention for breast cancer survivors. Support Care Cancer. Feb 2007;15(2):203-211. [CrossRef] [Medline]
    58. McNeil J, Brenner DR, Stone CR, et al. Activity tracker to prescribe various exercise intensities in breast cancer survivors. Med Sci Sports Exerc. May 2019;51(5):930-940. [CrossRef] [Medline]
    59. Mendoza JA, Baker KS, Moreno MA, et al. A Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: a pilot study. Pediatr Blood Cancer. Dec 2017;64(12):28618158. [CrossRef]
    60. Millstine DM, Bhagra A, Jenkins SM, et al. Use of a wearable EEG headband as a meditation device for women with newly diagnosed breast cancer: a randomized controlled trial. Integr Cancer Ther. 2019;18:1534735419878770. [CrossRef] [Medline]
    61. Pinto BM, Papandonatos GD, Goldstein MG. A randomized trial to promote physical activity among breast cancer patients. Health Psychol. Jun 2013;32(6):616-626. [CrossRef] [Medline]
    62. Pinto BM, Papandonatos GD, Goldstein MG, Marcus BH, Farrell N. Home-based physical activity intervention for colorectal cancer survivors. Psychooncology. Jan 2013;22(1):54-64. [CrossRef] [Medline]
    63. Pinto BM, Stein K, Dunsiger S. Peers promoting physical activity among breast cancer survivors: a randomized controlled trial. Health Psychol. May 2015;34(5):463-472. [CrossRef] [Medline]
    64. Pope ZC, Zeng N, Zhang R, Lee HY, Gao Z. Effectiveness of combined smartwatch and social media intervention on breast cancer survivor health outcomes: a 10-week pilot randomized trial. J Clin Med. Jun 7, 2018;7(6):140. [CrossRef] [Medline]
    65. Sajid S, Dale W, Mustian K, et al. Novel physical activity interventions for older patients with prostate cancer on hormone therapy: a pilot randomized study. J Geriatr Oncol. Mar 2016;7(2):71-80. [CrossRef] [Medline]
    66. Uhm KE, Yoo JS, Chung SH, et al. Effects of exercise intervention in breast cancer patients: is mobile health (mHealth) with pedometer more effective than conventional program using brochure? Breast Cancer Res Treat. Feb 2017;161(3):443-452. [CrossRef] [Medline]
    67. Valle CG, Diamond MA, Heiling HM, et al. Effect of an mHealth intervention on physical activity outcomes among young adult cancer survivors: the IMPACT randomized controlled trial. Cancer. Feb 1, 2023;129(3):461-472. [CrossRef] [Medline]
    68. Van Blarigan EL, Chan H, Van Loon K, et al. Self-monitoring and reminder text messages to increase physical activity in colorectal cancer survivors (Smart Pace): a pilot randomized controlled trial. BMC Cancer. Mar 11, 2019;19(1):218. [CrossRef] [Medline]
    69. Van Blarigan EL, Dhruva A, Atreya CE, et al. Feasibility and acceptability of a physical activity tracker and text messages to promote physical activity during chemotherapy for colorectal cancer: pilot randomized controlled trial (Smart Pace II). JMIR Cancer. Jan 11, 2022;8(1):e31576. [CrossRef] [Medline]
    70. Van Blarigan EL, Kenfield SA, Olshen A, et al. Effect of a home-based walking intervention on cardiopulmonary fitness and quality of life among men with prostate cancer on active surveillance: the active surveillance exercise randomized controlled trial. Eur Urol Oncol. Jun 2024;7(3):519-526. [CrossRef] [Medline]
    71. Weiner LS, Takemoto M, Godbole S, et al. Breast cancer survivors reduce accelerometer-measured sedentary time in an exercise intervention. J Cancer Surviv. Jun 2019;13(3):468-476. [CrossRef] [Medline]
    72. Pinto BM, Kindred M, Franco R, Simmons V, Hardin J. A “novel” multi-component approach to promote physical activity among older cancer survivors: a pilot randomized controlled trial. Acta Oncol. Aug 2021;60(8):968-975. [CrossRef] [Medline]
    73. Rastogi S, Tevaarwerk AJ, Sesto M, et al. Effect of a technology-supported physical activity intervention on health-related quality of life, sleep, and processes of behavior change in cancer survivors: a randomized controlled trial. Psychooncology. Nov 2020;29(11):1917-1926. [CrossRef] [Medline]
    74. Vallance JK, Nguyen NH, Moore MM, et al. Effects of the ACTIVity And TEchnology (ACTIVATE) intervention on health-related quality of life and fatigue outcomes in breast cancer survivors. Psychooncology. Jan 2020;29(1):204-211. [CrossRef] [Medline]
    75. Anderson AS, Dunlop J, Gallant S, et al. Feasibility study to assess the impact of a lifestyle intervention ('LivingWELL’) in people having an assessment of their family history of colorectal or breast cancer. BMJ Open. Feb 1, 2018;8(2):e019410. [CrossRef] [Medline]
    76. Walsh JC, Richmond J, Mc Sharry J, et al. Examining the impact of an mHealth behavior change intervention with a brief in-person component for cancer survivors with overweight or obesity: randomized controlled trial. JMIR Mhealth Uhealth. Jul 5, 2021;9(7):e24915. [CrossRef] [Medline]
    77. Bennett JA, Lyons KS, Winters-Stone K, Nail LM, Scherer J. Motivational interviewing to increase physical activity in long-term cancer survivors: a randomized controlled trial. Nurs Res. 2007;56(1):18-27. [CrossRef] [Medline]
    78. Hardcastle SJ, Maxwell-Smith C, Cavalheri V, et al. A randomized controlled trial of Promoting Physical Activity in Regional and Remote Cancer Survivors (PPARCS). J Sport Health Sci. Jan 2024;13(1):81-89. [CrossRef] [Medline]
    79. Frensham LJ, Parfitt G, Dollman J. Effect of a 12-week online walking intervention on health and quality of life in cancer survivors: a quasi-randomized controlled trial. Int J Environ Res Public Health. Sep 21, 2018;15(10):2081. [CrossRef] [Medline]
    80. Hardcastle SJ, Maxwell-Smith C, Hince D, et al. The wearable activity technology and action-planning trial in cancer survivors: physical activity maintenance post-intervention. J Sci Med Sport. Sep 2021;24(9):902-907. [CrossRef]
    81. Lynch BM, Nguyen NH, Moore MM, et al. A randomized controlled trial of a wearable technology-based intervention for increasing moderate to vigorous physical activity and reducing sedentary behavior in breast cancer survivors: the ACTIVATE trial. Cancer. Aug 15, 2019;125(16):2846-2855. [CrossRef] [Medline]
    82. Maxwell-Smith C, Hince D, Cohen PA, et al. A randomized controlled trial of WATAAP to promote physical activity in colorectal and endometrial cancer survivors. Psychooncology. Jul 2019;28(7):1420-1429. [CrossRef] [Medline]
    83. Nguyen NH, Vallance JK, Buman MP, et al. Effects of a wearable technology-based physical activity intervention on sleep quality in breast cancer survivors: the ACTIVATE trial. J Cancer Surviv. Apr 2021;15(2):273-280. [CrossRef] [Medline]
    84. Gehring K, Kloek CJ, Aaronson NK, et al. Feasibility of a home-based exercise intervention with remote guidance for patients with stable grade II and III gliomas: a pilot randomized controlled trial. Clin Rehabil. Mar 2018;32(3):352-366. [CrossRef] [Medline]
    85. Golsteijn RHJ, Bolman C, Volders E, Peels DA, de Vries H, Lechner L. Short-term efficacy of a computer-tailored physical activity intervention for prostate and colorectal cancer patients and survivors: a randomized controlled trial. Int J Behav Nutr Phys Act. Oct 30, 2018;15(1):106. [CrossRef] [Medline]
    86. Kim JY, Lee MK, Lee DH, et al. Effects of a 12-week home-based exercise program on quality of life, psychological health, and the level of physical activity in colorectal cancer survivors: a randomized controlled trial. Support Care Cancer. Aug 2019;27(8):2933-2940. [CrossRef] [Medline]
    87. Park YH, Lee JI, Lee JY, et al. Internet of things-based lifestyle intervention for prostate cancer patients on androgen deprivation therapy: a prospective, multicenter, randomized trial. Am J Cancer Res. 2021;11(11):5496-5507. [Medline]
    88. Li L, Wang L, Sun Q, et al. Effect of two interventions on sleep quality for adolescent and young adult cancer survivors: a pilot randomized controlled trial. Cancer Nurs. 2022;45(2):E560-E572. [CrossRef] [Medline]
    89. What is the difference between a smart bracelet and a smart band? WEMEDIA. URL: https:/​/www.​wemedia.it/​faq/​what_is_the_difference_between_a_smart_bracelet_and_a_smart_band-18208.​html [Accessed 2025-07-05]
    90. Rehman T. Smartwatch vs smart bracelet—what’s the difference? AskDifference. URL: https://www.askdifference.com/smartwatch-vs-smart-bracelet [Accessed 2025-07-05]
    91. Tudor-Locke C. Taking steps toward increased physical activity: using pedometers to measure and motivate. President’s Council on Physical Fitness and Sports: Research Digest; 2002. URL: https://scispace.com/pdf/taking-steps-toward-increased-physical-activity-using-3mxyjdbrot.pdf [Accessed 2025-07-29]
    92. Pindus DM, Selzer-Ninomiya A, Nayak A, Pionke JJ, Raine LB. Effects of reducing sedentary behaviour duration by increasing physical activity, on cognitive function, brain function and structure across the lifespan: a systematic review protocol. BMJ Open. Oct 2022;12(10):e046077. [CrossRef]
    93. Creasy SA, Ostendorf DM, Kaizer L, et al. Effect of physical activity on changes in weight and aerobic capacity during an 18-month behavioral weight loss intervention. Int J Behav Nutr Phys Act. May 21, 2025;22(1):57. [CrossRef] [Medline]
    94. Oppert JM, Bellicha A, van Baak MA, et al. Exercise training in the management of overweight and obesity in adults: synthesis of the evidence and recommendations from the European Association for the Study of Obesity Physical Activity Working Group. Obes Rev. Jul 2021;22 Suppl 4(Suppl 4):e13273. [CrossRef] [Medline]
    95. Foster-Schubert KE, Alfano CM, Duggan CR, et al. Effect of diet and exercise, alone or combined, on weight and body composition in overweight-to-obese postmenopausal women. Obesity (Silver Spring). Aug 2012;20(8):1628-1638. [CrossRef] [Medline]
    96. Cataldo R, John J, Chandran L, Pati S, Shroyer ALW. Impact of physical activity intervention programs on self-efficacy in youths: a systematic review. ISRN Obes. 2013;2013:586497. [CrossRef] [Medline]
    97. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38-48. [CrossRef] [Medline]
    98. Goldschmidt S, Schmidt ME, Steindorf K. Long-term effects of exercise interventions on physical activity in breast cancer patients: a systematic review and meta-analysis of randomized controlled trials. Support Care Cancer. Jan 24, 2023;31(2):130. [CrossRef] [Medline]
    99. Mokhtari-Hessari P, Montazeri A. Health-related quality of life in breast cancer patients: review of reviews from 2008 to 2018. Health Qual Life Outcomes. Oct 12, 2020;18(1):338. [CrossRef] [Medline]
    100. Awick EA, Ehlers DK, Aguiñaga S, Daugherty AM, Kramer AF, McAuley E. Effects of a randomized exercise trial on physical activity, psychological distress and quality of life in older adults. Gen Hosp Psychiatry. Nov 2017;49:44-50. [CrossRef] [Medline]
    101. Zainordin NH, A Karim N, Shahril MR, Abd Talib R. Physical activity, sitting time, and quality of life among breast and gynaecology cancer survivors. Asian Pac J Cancer Prev. Aug 1, 2021;22(8):2399-2408. [CrossRef] [Medline]
    102. Zhang Y, Li J, Zhang Y, et al. Mediating effect of social support between caregiver burden and quality of life among family caregivers of cancer patients in palliative care units. Eur J Oncol Nurs. Feb 2024;68:102509. [CrossRef] [Medline]
    103. Qiao X, Ji L, Jin Y, et al. Effectiveness of a theory-underpinning exercise intervention among community-dwelling (pre)frail older adults: a stepped-wedge cluster-randomized trial. Int J Nurs Stud. Jan 2025;161:104933. [CrossRef] [Medline]
    104. Global status report on physical activity 2022. World Health Organization (WHO). 2022. URL: https:/​/www.​who.int/​teams/​health-promotion/​physical-activity/​global-status-report-on-physical-activity-2022 [Accessed 2025-07-05]
    105. Keats MR, Yu X, Sweeney Magee M, et al. Use of wearable activity-monitoring technologies to promote physical activity in cancer survivors: challenges and opportunities for improved cancer care. Int J Environ Res Public Health. Mar 8, 2023;20(6):36981693. [CrossRef] [Medline]

    ‎
    HCP: health care professional
    MeSH: Medical Subject Headings
    mHealth: mobile health
    MVPA: moderate-to-vigorous-intensity physical activity
    PA: physical activity
    PICOS: Participants, Interventions, Comparisons, Outcomes, and Study Design
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    QoL: quality of life
    SMD: standard mean difference
    WED: wearable electronic device
    WEDS: wearable electronic device system

    Edited by Javad Sarvestan; submitted 23.03.25; peer-reviewed by Antonio Martinko, Frans Van der Ouderaa; final revised version received 09.06.25; accepted 10.06.25; published 14.08.25.

    Copyright

    © Zezhang Wang, Yunhuan Li, Qian Wang, Yonglin Su. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.8.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.