Background: Physical activity (PA) interventions can increase PA and improve well-being among adults affected by cancer; however, most adults do not meet cancer-specific PA recommendations. Lack of time, facility access, and travel distances are barriers to participation in PA interventions. eHealth technologies may address some of these barriers, serving as a viable way to promote PA behavior change in this population. However, no review from July 2018 has synthesized available evidence across eHealth and cancer types or examined the use of behavioral theory and behavior change techniques (BCTs), leaving important gaps in knowledge.
Objective: This review aims to provide a comprehensive, updated overview of evidence on eHealth PA interventions for adults with cancer by describing the current state of the literature, exploring associations between intervention characteristics and effectiveness, and identifying future research needs.
Methods: MEDLINE, Embase, CINAHL, SportDiscus, Scopus, and CENTRAL were searched for eHealth PA interventions for adults affected by cancer. Study selection and data extraction were performed in duplicate, with consultation from the senior author (NCR). BCT coding, risk of bias, and completeness of reporting were performed using standardized tools. Results were summarized via narrative synthesis and harvest plots. Weight analyses were conducted to explore the associations between intervention characteristics and effectiveness.
Results: A total of 71 articles (67 studies) involving 6655 participants (mean age 56.7 years, SD 8.2) were included. Nearly 50% (32/67) of the articles were published after July 2018. Significant postintervention PA increases were noted in 52% (35/67) of the studies, and PA maintenance was noted in 41% (5/12) of the studies that included a follow-up. Study duration, primary objectives, and eHealth modality (eg, websites, activity trackers, and SMS text messaging) varied widely. Social cognitive theory (23/67, 34%) was the most used theory. The mean number of BCTs used across the studies was 13.5 (SD 5.5), with self-monitoring, credible sources, and goal setting being used in >90% of studies. Weight analyses showed the greatest associations between increased PA levels and PA as a primary outcome (0.621), interventions using websites (0.656) or mobile apps (0.563), interventions integrating multiple behavioral theories (0.750), and interventions using BCTs of problem solving (0.657) and action planning (0.645). All studies had concerns with high risk of bias, mostly because of the risk of confounding, measurement bias, and incomplete reporting.
Conclusions: A range of eHealth PA interventions may increase PA levels among adults affected by cancer, and specific components (eg, websites, use of theory, and action planning) may be linked to greater effectiveness. However, more work is needed to ascertain and optimize effectiveness, measure long-term effects, and address concerns with bias and incomplete reporting. This evidence is required to support arguments for integrating eHealth within PA promotion in oncology.
Physical activity (PA) can improve physical and psychosocial well-being among adults diagnosed with cancer. Benefits reported throughout the cancer trajectory (ie, from diagnosis onward) include enhanced physical functioning and quality of life, as well as reduced negative effects of cancer and treatment-related side effects . Consequently, cancer-specific PA guidelines have been published, recommending at least 90 minutes of weekly moderate-intensity aerobic PA (note: before 2019, 150 minutes were recommended) and strength training for ≥2 days each week [ , ]. These guidelines have also been endorsed by leading cancer support organizations [ ]. Despite this evidence, most adults diagnosed with cancer do not achieve the recommended PA levels [ ].
Thus, developing and testing interventions to increase PA levels is a priority. As described in recent systematic reviews and meta-analyses, most interventions designed to enhance PA levels among individuals with cancer have been delivered face-to-face in fitness facilities, and findings suggest that such interventions can enhance physical and psychosocial well-being . However, among adults diagnosed with cancer, barriers such as lack of time, limited access to facilities, and travel distances can hinder participation in face-to-face PA interventions [ ]. Barriers to PA have been exacerbated during the COVID-19 pandemic, with most face-to-face PA opportunities being limited or canceled and adults with cancer reporting decreased PA and increased sedentary time [ ].
eHealth technologies, including telephones, websites, email, and mobile health (mHealth) technologies (eg, SMS text messaging, smartphones, wearable technology, and apps) may be useful to address some of these barriers to PA and reach a wider audience of adults living with cancer [- ]. The prevalence of and preference for using eHealth is increasing rapidly among adults with cancer, with the National Cancer Institute prioritizing research into the effective use of eHealth in the context of PA promotion for adults with cancer [ - ]. Reviews summarizing the effects of eHealth to promote PA in adults with cancer suggest that technology-supported PA interventions may enhance PA levels and health-related quality of life and decrease fatigue [ - ]. Notwithstanding the evidence to date, important gaps in knowledge remain. First, only studies published before July 2018 have been reviewed. As the field of eHealth PA interventions is rapidly growing and evolving, an update is needed. Second, reviews have had limited scope with regard to study design (eg, randomized controlled trials [RCTs] only [ ]), population (eg, women with breast cancer only [ ]), and technology components (eg, activity trackers or mHealth only [ , , ]). Expanding eligibility criteria to include various study designs, cancer types, and the full range of eHealth technologies is required to provide a more comprehensive overview of the effects of eHealth PA interventions in oncology. Finally, despite evidence supporting the role of behavior change techniques (BCTs) and theories (eg, theory of planned behavior) in PA interventions, the integration of BCTs and theory with eHealth PA interventions has received limited attention [ , , - ]. Roberts et al [ ] examined the use of theory and BCTs for 15 eHealth PA interventions published before November 2016, whereas Kiss et al [ ] coded BCTs for 16 interventions, many of which were duplicates from Roberts et al [ ], published before July 2018.
Thus, the purpose of this review is to summarize evidence on the use of eHealth to support PA behavior change among adults diagnosed with cancer. The specific objectives are to (1) describe the current state of the literature on the effectiveness of eHealth in supporting PA behavior change (pre- to postintervention and follow-ups, where available), (2) explore intervention characteristics that may promote PA behavior change (eg, eHealth components, use of theory, and BCTs), and (3) identify research needs for future work.
The review protocol was registered prospectively via PROSPERO (International Prospective Register of Systematic Reviews): CRD42020162181. Reporting of the results follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews .
For identifying relevant studies, a search strategy covering the major topics of health technology, cancer, and PA was developed in MEDLINE (R) using existing reviews to guide the selection of search terms. It was then refined, finalized, and translated to the other databases used herein with the help of a university librarian (Table S1 in). MEDLINE (R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily (OVID), Embase (OVID), CENTRAL (OVID), CINAHL (EBSCO), Sport Discus (EBSCO), and Scopus were searched from database inception through to December 18, 2019. This search was updated on January 7, 2021.
To be included, articles had to (1) comprise adult participants aged ≥18 years diagnosed with cancer, (2) evaluate a PA intervention that used technology (mobile app, SMS text messages, wearable activity tracker, website, email, or other eHealth) as an active component in the intervention to support behavior change, (3) measure and report on PA levels (objectively or subjectively), (4) be published in English, and (5) be published in a peer-reviewed journal (conference abstracts and gray literature were not included). Articles were excluded if they (1) involved adults whose only cancer diagnosis occurred during childhood, adults without a history of cancer, or caregivers; (2) used telephone contact as the only technology component in the intervention; (3) used technology for the measurement of outcomes only (eg, accelerometer for PA measurement pre- or postintervention); (4) lacked a PA intervention (eg, observational study of PA behavior); (5) reported ongoing trials without full results being available (ie, protocols); and (6) the full text was unavailable. Interventions could be either partially supervised (ie, some human contact) or unsupervised (ie, entirely automated), and the amount of technology use within interventions was not quantified.
After importing all search results into EndNote X9.2 (Clarivate Analytics), the first author conducted automatic and subsequent manual deduplication. Unique articles were exported to Rayyan (Rayyan Systems) for screening according to the eligibility criteria . Title and abstract screening were conducted concurrently by the first author by removing all articles that did not meet the criteria. Articles with titles and abstracts that lacked enough information to make a decision were carried forward to the full-text screening stage. Full texts of the remaining articles were obtained and screened independently by the first (ME) and second authors (MME), who recorded their decisions as well as reasons for exclusion where applicable. The 2 authors then met to discuss the decisions and resolve disagreements based on additional reviews of the articles. Disagreements that could not be resolved directly were resolved via discussion with the senior author (NCR) to yield the final list of included articles.
Before data extraction, a standardized data extraction table was developed and refined using 3 test articles. The final data extraction table included (1) participant information (age, cancer diagnosis, and eligibility criteria), (2) study design (timing, eligibility and recruitment rates, and recruitment methods), (3) intervention details (groups, objectives, duration, active components, technology integration, BCTs according to the Michie behavior change taxonomy comprising 93 BCTs across 16 categories , and use of theory), (4) outcomes (participant numbers, demographics, primary and secondary outcomes, PA-related outcomes, adherence or completion to intervention, and technology use), and (5) additional factors (key findings, challenges, and limitations). It was decided that theory would be recorded only when explicitly described in the included studies. Data were then extracted independently by the first (ME) and second authors (MME), with each author being responsible for half the number of articles. For confirming the reliability of the extraction, 5 random articles were exchanged between authors, extracted a second time, and the data were compared between extractions. Because of minor discrepancies, coding of BCTs was repeated for all articles, and discussions were held between the first and second authors to reach a consensus. The authors did not complete BCT coder training before BCT coding. No other discrepancies were noted. Any missing information was denoted using the phrase not reported in the data extraction table. Attempts were made to fill in missing information via protocol papers and other related publications for each study. The authors of the included articles were not contacted directly for additional information.
Risk of Bias and Completeness of Reporting
The Cochrane risk of bias (RoB) tool (RoB-2) was used for multiarm interventions, which included evaluations for RoB in five domains: (1) randomization, (2) deviation from the intended intervention, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of reported results . The ROBINS-I (RoB in nonrandomized studies of interventions) tool, which evaluates bias across seven domains: (1) confounding, (2) participant selection, (3) classification of intervention, (4) deviation from intended intervention, (5) missing data, (6) outcome measurement, and (7) selection of reported results was used for single-arm designs [ ]. An overall RoB was given according to the highest RoB rating in any domain for each study. For example, a study with high RoB in domain 1 and low RoB across all other domains received a high overall RoB rating. The completeness of reporting was evaluated using the CONSORT (Consolidated Standards of Reporting Trials)–eHealth checklist, with items assessed as reported, not reported, or not applicable [ ]. The completeness of reporting score was calculated for each article as the percentage of applicable items that were reported. These assessments were performed independently by the first (ME) and second authors (MME). Verification was performed by exchanging 5 random articles between authors for repeat assessment, and no discrepancies were documented.
Data Synthesis and Analysis
To summarize the data extracted from each article, descriptive statistics were calculated for participant demographics, adherence, and completion. Intervention details were categorized and summarized, whereas results were converted to standardized metrics where possible to enable comparison across studies. Because of the substantial heterogeneity of the studies with regards to population, intervention, comparison, and outcome, meta-analyses were not performed. Instead, extracted data across studies were summarized using narrative synthesis techniques, and summary tables were presented . Harvest plots were created to provide a visual summary of study effects on PA outcomes, including PA levels directly postintervention and PA maintenance at follow-up, providing an overview of intervention effectiveness on PA levels [ ]. Following recommendations, harvest plots were prepared with studies grouped according to the statistical significance of their PA outcomes (PA increase, PA decrease, or no change) [ ]. Bar heights were used to distinguish between RCTs (high) and other study designs (low), whereas shading was used to specify how PA was measured (subjective, objective, or both). For addressing objective 2, weight analyses were conducted to explore associations between independent variables (intervention characteristics: use of supervised elements, various types of eHealth, theory, and BCTs) and the dependent variable (PA levels) [ ]. Weight was calculated for each independent or dependent variable pair by dividing the number of studies featuring each independent variable and reporting a significant improvement in the dependent variable by the total number of studies featuring the independent variable. Weights range from 0-1, with a higher value indicating a stronger association between the independent variable and significant changes in PA levels. Weights are presented to three decimal places and are equivalent to percentages (ie, 0.123 could also be read as 12.3%). The weight for each independent or dependent variable pairing was then compared with the overall weight for all studies to explore if the presence of certain intervention characteristics was associated with a higher weight (ie, more often linked with significant changes in PA levels). For continuous independent variables (duration and number of BCTs used), studies were grouped according to the mean value (greater than or less than the mean). For BCTs, weights were only calculated for the most common BCTs or BCT categories (ie, used in at least 50% of interventions) to minimize the introduction of further bias when calculating weights using only a small number of independent or dependent variable pairs [ ].
After deduplication, 4022 citations were screened at the title or abstract level; of the 4022 citations, 3873 (96.29%) were removed as they did not meet the eligibility criteria. During full-text screening, the agreement between the first 2 authors on the 145 articles was 82.1%, with decisions for articles where no agreement was reached (26/145, 17.9%) being resolved via discussion with the senior author (NCR). Of the 145 articles, 74 (51.0%) articles were excluded during full-text screening, and, overall, 71 (49.0%) articles representing 67 unique studies were included [- ]. presents an overview of the study selection, with reasons for article exclusion. The remainder of the results are presented according to the number of unique studies (n=67). and provide more information on each of the included studies and their respective PA interventions.
|Reference||Study type||Participant characteristics||Study outcomes|
|Mayo et al ||RCTb|
|Maxwell-Smith et al ||RCT|
|Park et al ||RCT|
|Gomersall et al ||RCT|
|Gehring et al ||RCT|
|Singh et al ||RCT|
|Buscemi et al ||RCT|
|Chapman et al ||RCT|
|Fazzino et al ||RCT|
|Hartman et al ||RCT|
|Hatchett et al ||RCT|
|Lynch et al [, ]||RCT|
|McNeil et al ||RCT|
|Park et al ||RCT|
|Paxton et al ||RCT|
|Pope et al ||RCT|
|Short et al ||RCT|
|Uhm et al ||RCT|
|Weiner et al ||RCT|
|Allicock et al ||RCT|
|Gokal et al ||RCT|
|Van Blarigan et al ||RCT|
|Haggerty et al ||RCT|
|Chow et al ||RCT|
|Edbrooke et al ||RCT|
|Cox et al ||RCT|
|Forbes et al ||RCT|
|Golsteijn et al ||RCT|
|Ormel et al ||RCT|
|Webb et al [, ]||RCT|
|Bantum et al ||RCT|
|Frensham et al [, ]||RCT|
|Gell et al ||RCT|
|Kanera et al [, ]||RCT|
|Mayer et al ||RCT|
|Park et al ||RCT|
|Valle et al ||RCT|
|Rabin et al ||RCT|
|Robertson et al ||RCT|
|Yun et al ||RCT|
|Shang et al ||RCT|
|Villaron et al ||RCT|
|Chan et al ||RCT|
|Kenfield et al ||RCT|
|Alibhai et al ||RCT|
|Bade et al ||Other|
|Naito et al ||Other|
|Befort et al ||Other|
|Nápoles et al ||Other|
|Pope et al ||Other|
|Spark et al ||Other|
|Wilson et al ||Other|
|Chung et al ||Other|
|Nyrop et al ||Other|
|Cairo et al ||Other|
|Cheong et al ||Other|
|Groen et al ||Other|
|Hong et al ||Other|
|McCarroll et al ||Other|
|MacDonald et al ||Other|
|Gell et al ||Other|
|Puszkiewicz et al ||Other|
|Short et al ||Other|
|Abbott et al ||Other|
|Javaheri et al ||Other|
|Zhang et al ||Other|
|Trinh et al ||Other|
aStudies were sorted by study type, cancer type, and treatment. Of note, some articles did not report certain participant characteristics, such as ethnicity or age.
bRCT: randomized controlled trial.
cPA: physical activity.
|Reference||Intervention design||PAb||Delivery||Theory||Total number of BCTc/ number of BCT categories covered|
|Mayo et al ||Duration (weeks): 16; follow-up (weeks): 24; no supervision||Objective||WATd and phone||Exercise goal or program and phone counseling||Theory on etiology and treatment of cancer-related fatigue||13/8|
|Maxwell-Smith et al ||Duration (weeks): 12; partial supervision||Objective||Website, WAT, and SMS text messaging||Print materials, phone counseling, in-person counseling, and group interaction||HAPAe||15/9|
|Park et al ||Duration (weeks): 8; partial supervision||Subjective and objective||SMS text messaging||PA log, print materials, and in-person counseling||SDTf||14/9|
|Gomersall et al ||Duration (weeks): 12; partial supervision||Subjective and objective||SMS text messaging||Exercise goal or program and in-person counseling||SCTg||16/10|
|Gehring et al ||Duration (weeks): 26; partial supervision||Subjective||Website, WAT, and email||PA log, print materials, and in-person counseling||None||9/5|
|Singh et al ||Duration (weeks): 12; partial supervision||Subjective and objective||Website and WAT||Print materials and in-person counseling||TPBh||7/5|
|Buscemi et al ||Duration (weeks): 6; no supervision||Subjective||SMS text messaging and mobile app||Phone counseling||None||6/5|
|Chapman et al ||Duration (weeks): 4; follow-up (weeks): 12; no supervision||Subjective||Website||None||TTMi||6/2|
|Fazzino et al ||Duration (weeks): 52; no supervision||Subjective and objective||WAT and phone||Exercise goal or program, PA log, phone counseling, group interaction, and DVD||SCT||11/8|
|Hartman et al ||Duration (weeks): 12; partial supervision||Objective||Website, WAT, email, and phone||Exercise goal or program, phone counseling, and in-person counseling||TTM and SCT||13/8|
|Hatchett et al ||Duration (weeks): 12; no supervision||Subjective||None||SCT||16/10|
|Lynch et al [, ]||Duration (weeks): 12; partial supervision||Objective||Website and WAT||Exercise goal or program, print materials, phone counseling, and in-person counseling||Behavior change strategies||16/8|
|McNeil et al ||Duration (weeks): 12; follow-up (weeks): 24; no supervision||Objective||WAT, email, and phone||PA log and phone counseling||None||13/7|
|Park et al ||Duration (weeks): 12; no supervision||Subjective||WAT and mobile app||Exercise goal or program||None||11/7|
|Paxton et al ||Duration (weeks): 12; no supervision||Subjective||Website and email||Exercise goal or program||SCT, TTM, goal-setting theory, and social marketing||24/12|
|Pope et al ||Duration (weeks): 10; no supervision||Objective||Website and WAT||Exercise goal or program and group interaction||SCT||21/12|
|Short et al ||Duration (weeks): 12; no supervision||Subjective||Website and email||None||SCT||18/11|
|Uhm et al ||Duration (weeks): 12; no supervision||Subjective||WAT and mobile app||Exercise goal or program||None||14/9|
|Weiner et al ||Duration (weeks): 12; no supervision||Objective||WAT, email, and phone||Phone counseling and in-person counseling||SCT||17/10|
|Allicock et al ||Duration (weeks): 4; no supervision||Subjective and objective||SMS text messaging and mobile app||PA log and print materials||SCT||9/8|
|Gokal et al ||Duration (weeks): 12; no supervision||Subjective and objective||WAT||PA log||TPB||12/8|
|Van Blarigan et al ||Duration (weeks): 12; partial supervision||Objective||Website, WAT, and SMS text messaging||Print materials||TPB||12/9|
|Haggerty et al ||Duration (weeks): 24; no supervision||Subjective||Website, SMS text messaging, and phone||Exercise goal or program and PA log||None||15/8|
|Chow et al ||Duration (weeks): 16; no supervision||Subjective and objective||WAT, email, SMS text messaging, mobile app, and phone||Phone counseling and group interaction||SDT||12/6|
|Edbrooke et al ||Duration (weeks): 8; follow-up (weeks): 26; partial supervision||Objective||WAT, SMS text messaging, and phone||Exercise goal or program, PA log, phone counseling, in-person counseling, and DVD||None||18/11|
|Cox et al ||Duration (weeks): 26; no supervision||Subjective and objective||Website, WAT, email, and phone||Exercise goal or program and group interaction||SCT and TTM||8/6|
|Forbes et al ||Duration (weeks): 9; no supervision||Subjective||Website and email||None||Unspecified theory-based||16/10|
|Golsteijn et al ||Duration (weeks): 26; follow-up (weeks): 16; no supervision||Subjective and objective||Website and WAT||None||SCT, TTM, HAPA, I-Change model, and health belief model||16/10|
|Ormel et al ||Duration (weeks): 12; no supervision||Subjective||Email, mobile app, and phone||PA log and phone counseling||None||9/7|
|Webb et al [, ]||Duration (weeks): 12; follow-up (weeks): 24; no supervision||Subjective||Website||PA log, print materials, group interaction, and DVD||SCT and TPB||24/12|
|Bantum et al ||Duration (weeks): 6; no supervision||Subjective||Website and phone||Print materials and group interaction||None||18/10|
|Frensham et al [, ]||Duration (weeks): 12; follow-up (weeks): 24; no supervision||Objective||Website and WAT||Exercise goal or program, PA log, and group interaction||SCT||9/5|
|Gell et al ||Duration (weeks): 8; partial supervision||Objective||Website, WAT, SMS text messaging, and phone||In-person counseling||SCT||11/6|
|Kanera et al [, ]||Duration (weeks): 26; no supervision||Subjective||Website and email||None||SCT||14/7|
|Mayer et al ||Duration (weeks): 26; no supervision||Subjective||WAT, mobile app, and phone||Print materials, phone counseling, and group interaction||SDT||16/10|
|Park et al ||Duration (weeks): 4; no supervision||Subjective||WAT||Exercise goal or program, PA log, and DVD||None||10/8|
|Valle et al ||Duration (weeks): 12; no supervision||Subjective||Website||Exercise goal or program, PA log, and group interaction||SCT||19/11|
|Rabin et al ||Duration (weeks): 12; no supervision||Subjective||Website and email||None||SCT and TTM||14/9|
|Robertson et al ||Duration (weeks): 4; no supervision||Subjective and objective||Website, WAT, SMS text messaging, and mobile app||None||SDT, behavior change wheel, and motivational interviewing||23/14|
|Yun et al ||Duration (weeks): 12; follow-up (weeks): 24; partial supervision||Subjective||Website and phone||Print materials, phone counseling, and in-person counseling||None||10/6|
|Shang et al ||Duration (weeks): 12; no supervision||Subjective and objective||WAT and phone||Exercise goal or program, PA log, and phone counseling||None||14/8|
|Villaron et al ||Duration (weeks): 8; no supervision||Objective||WAT and SMS text messaging||Print materials||None||11/8|
|Chan et al ||Duration (weeks): 12; follow-up (weeks): 24; no supervision||Subjective||Website, WAT, SMS text messaging, and phone||Phone counseling||SCT||10/8|
|Kenfield et al ||Duration (weeks): 12; no supervision||Subjective and objective||Website, WAT, email, and SMS text messaging||Exercise goal or program||TPB||18/10|
|Alibhai et al ||Duration (weeks): 26; partial supervision||Subjective and objective||WAT, mobile app, and phone||Exercise goal or program, phone counseling, and group interaction||None||11/9|
|Bade et al ||Duration (weeks): 4; no supervision||Objective||WAT, SMS text messaging, and phone||Phone counseling||Prospect theory and gain-framed messaging||11/7|
|Naito et al ||Duration (weeks): 8; partial supervision||Objective||WAT||Exercise goal or program and in-person counseling||None||12/7|
|Befort et al ||Duration (weeks): 26; no supervision||Subjective||WAT and phone||Exercise goal or program, PA log, phone counseling, group interaction, and DVD||SCT||13/9|
|Nápoles et al ||Duration (weeks): 8; no supervision||Objective||WAT, mobile app, and phone||Print materials and phone counseling||SCT||11/7|
|Pope et al ||Duration (weeks): 10; no supervision||Objective||Mobile app||Group interaction||SCT||9/6|
|Spark et al ||Duration (weeks): 26; follow-up (weeks): 52; no supervision||Objective||SMS text messaging||Phone counseling||None||15/7|
|Wilson et al ||Duration (weeks): 8; partial supervision||Objective||WAT||Exercise goal or program and group interaction||Health belief model||9/7|
|Chung et al ||Duration (weeks): 6; no supervision||Objective||Mobile app||PA log and group interaction||None||5/5|
|Nyrop et al ||Duration (weeks): 12; no supervision||Subjective and objective||Website and WAT||PA log and print materials||None||5/5|
|Cairo et al ||Duration (weeks): 24; no supervision||Subjective||SMS text messaging and mobile app||Print materials and DVD||None||5/5|
|Cheong et al ||Duration (weeks): 12; no supervision||Subjective||WAT and mobile app||Exercise goal or program||None||16/10|
|Groen et al ||Duration (weeks): 16; no supervision||Subjective||Website||None||None||10/6|
|Hong et al ||Duration (weeks): 10; no supervision||Subjective||Website||None||Goal-setting theory||—j|
|McCarroll et al ||Duration (weeks): 4; no supervision||Subjective||Mobile app||None||SCT||13/8|
|MacDonald et al ||Duration (weeks): 8; follow-up (weeks): 20; no supervision||Subjective||Website, WAT, mobile app, and phone||Exercise goal or program and phone counseling||Motivational interviewing and cognitive behavioral therapy||42/12|
|Gell et al ||Duration (weeks): 4; partial supervision||Objective||Website, WAT, SMS text messaging, and phone||Phone counseling and in-person counseling||SCT||14/8|
|Puszkiewicz et al ||Duration (weeks): 6; no supervision||Subjective||Mobile app||None||None||14/10|
|Short et al ||Duration (weeks): 2; partial supervision||Subjective||Email and mobile app||Phone counseling and in-person counseling||None||9/6|
|Abbott et al ||Duration (weeks): 12; partial supervision||Subjective||WAT and SMS text messaging||PA log, print materials, and in-person counseling||Gain-framed messaging||12/9|
|Javaheri et al ||Duration (weeks): 4; partial supervision||Objective||WAT and phone||Exercise goal or program, PA log, print materials, phone counseling, and in-person counseling||None||9/6|
|Zhang et al ||Duration (weeks): 26; partial supervision||Subjective and objective||Website, WAT, and phone||Exercise goal or program, phone counseling, group interaction, and DVD||None||8/7|
|Trinh et al ||Duration (weeks): 12; follow-up (weeks): 24; partial supervision||Objective||Website and WAT||None||None||14/8|
aStudies were sorted by study type, cancer type, and treatment. The follow-up duration is listed as total duration in weeks from baseline. Behavior change techniques (BCTs) are listed as the total number of BCTs and the number of BCT categories covered.
bPA: physical activity.
cBCT: behavior change technique.
dWAT: wearable activity tracker.
eHAPA: health action process approach.
fSDT: social determination theory.
gSCT: social cognitive theory.
hTPB: theory of planned behavior.
iTTM: transtheoretical model.
jDid not provide sufficient details to code BCTs.
Current State of the Literature
Studies were conducted in 8 different countries: United States (34/67, 51%), Australia (9/67, 13%), Canada (7/67, 10%), South Korea (7/67, 10%), The Netherlands (5/67, 8%), the United Kingdom (3/67, 5%), Japan (1/67, 2%), and France (1/67, 2%). Almost 50% of the articles (32/67, 48%) were published after July 2018 (Figure S1,).
A total of 6655 participants were enrolled across 67 studies with a median sample size of 51 (range 10-492). Participants were, on average, 56.7 (SD 8.2) years old. Approximately one in 3 studies recruited breast cancer survivors (24/67, 38%) or included multiple cancer types (23/67, 34%); 57% (38/67) of studies including only those who had completed treatment. Ethnicity was reported in 60% (40/67) of the studies, and 79.2% (SD 28.1%) of the participants were Caucasian. Only 9% (6/67) of the studies intentionally recruited non-Caucasian participants.
Study or Intervention Design
Approximately 67% (45/67) of studies used randomized trial designs with ≥2 study groups, whereas the remaining 33% (22/67) were nonrandomized single or two-arm trials. Across studies, the duration ranged from 1-52 weeks, with a median of 12 weeks. A total of 12 (18%) studies reported outcomes at a follow-up time point to assess the maintenance of intervention effects. Although all articles listed PA as an objective, their primary objectives varied widely. PA was the primary outcome of interest in 43% (29/67) of the studies. Other primary outcomes included feasibility (26/67, 39%), physical function (5/67, 8%), psychosocial function (4/67, 6%), and fatigue (3/67, 5%).
All the described interventions were either partially supervised (18/67, 27%), with both in-person and unsupervised components, or fully unsupervised (49/67, 72%). The interventions used between one and five technology components, with two (27/67, 40%) being the most common. Wearable devices (41/67, 61%) and websites (32/67, 48%) were the most frequently used technology components for delivering intervention content. Other common technology components used were SMS text messages (19/67, 28%), mobile apps (18/67, 27%), and email (15/67, 22%). Telephone contact was used in 37% (25/67) of the interventions. Figure S2 inpresents the trends in eHealth used in the included studies over time. A specific exercise program or prescription was provided in 37% (25/67) of the studies, whereas PA logs were used in 28% (19/67). Instructions via print materials (16/67, 24%) and DVD (7/67, 10%) were less common. Finally, many studies provided additional interaction via phone counseling (25/67, 37%), in-person counseling (16/67, 24%), or group-based formats (16/67, 24%).
Use of Theory and BCTs
More than one-third of the trials (26/67, 39%) did not report using behavioral theories to guide intervention design. Of the remaining studies, 34% (23/67) used social cognitive theory, 9% (6/67) used the transtheoretical model, and 9% (6/67) used the theory of planned behavior, whereas various other theories were applied in 25% (17/67) of studies [- ].
With respect to BCTs, across all studies, 69% (64/93) BCTs (covering 15 of 16 categories) were implemented at least once . The number of techniques applied ranged from 5-42, across 2-14 categories of the behavior change taxonomy, with 9 (8/67, 12%) being the most common. The frequency of use of the most common BCTs and all behavior change categories used are displayed in Figure S3 of . The four techniques (self-monitoring of behavior, credible source, goal-setting of behavior, and adding objects to the environment) and four categories (goals and planning, feedback and monitoring, antecedents, and comparison of outcomes) were found in >90% of the studies. In contrast, the prevalence of four categories (regulation, scheduled consequences, covert learning, and identity) was <10%.
The measurement of PA was highly variable across studies. Subjective PA measures were used in 45% (30/67) of the studies, whereas 33% (22/67) used objective measures, and the remaining 22% (15/67) used both. The subjective PA questionnaires used were the Godin Leisure Time Exercise Questionnaire (16/67, 24%), International PA Questionnaire (10/67, 15%), as well as 17 other questionnaires (19/67, 28%) [, ]. Accelerometers and pedometers were used to measure PA objectively in 39% (26/67) and 10% (7/67) of the studies, respectively. These included both research-grade and commercial sensors.
As seen in, statistically significant postintervention improvements in PA behavior were reported in 52% (35/67; 18 between-group, 17 within-group) of interventions. The remaining 32 interventions reported in no change (29/67, 43%), decreases in PA (1/67, 2%), or did not report on statistical significance (2/67, 3%). Studies that found statistically significant changes in PA, as well as those that did not, included participants with mixed cancer types, stages, and treatment status. The only intervention where PA decreased significantly was a 52-week RCT for patients with off-treatment breast cancer [ ]. Only 18% (12/67) of interventions tracked participants beyond the intervention (ie, between 12 and 52 weeks postintervention) to assess PA maintenance. Significant improvements in PA behavior were measured in 42% (5/67; 4 measured significant improvements directly postintervention) of the studies at the follow-up assessment ( ). The remaining 58% (7/67; 4 measured significant improvements directly postintervention) of the studies reported no change.
Intervention Characteristics That May Promote PA Behavior Change: Weight Analysis
Primary Outcomes and Supervision
The results of the weight analyses, which were used to explore associations between intervention elements and PA outcomes, are presented in. Studies with PA as the primary outcome (29/67, 43%) had a weight of 0.621, compared with 0.447 when PA was a secondary outcome (38/67, 57%). Interventions that were unsupervised (ie, no in-person elements during the intervention period; 50/67, 75%) had a weight of 0.560, whereas those with some supervision (17/67, 25%) had a weight of 0.412.
When a wearable device (40/67, 60%) or app (16/67, 24%) was used in an intervention, the weights were 0.525 and 0.563, respectively, as compared with a weight of 0.522 across all 67 studies. The use of websites as part of the intervention was associated with a weight of 0.656 (32/67, 48%), whereas SMS text messaging (0.368; 19/67, 28%), email (0.467; 15/67, 22%), and the use of multiple technologies (0.490; 51/67, 76%) had lower weights.
Use of Theory
The use of any behavioral theory in an intervention (41/67, 61%) was associated with a weight of 0.528, whereas interventions that did not report the use of theory (26/67, 39%) had a weight of 0.500. The most common theories, social cognitive theory (23/67, 34%; 0.565), transtheoretical model (6/67, 9%; 0.667), and theory of planned behavior (6/67, 9%; 0.667), were all associated with weights >0.522 [- ]. When multiple theories were used in a single intervention (8/67, 12%), the weight increased to 0.750. The weights for other theories were not calculated because of the small number of studies using each one.
Behavior Change Techniques
The weight of 46% (31/67) of the interventions that incorporated more than the mean number of 13.5 BCTs was 0.581, whereas the weight of the 52% (35/67) of the interventions that used less than 13.5 BCTs was 0.486. Among the 14 BCTs used in at least 45% of the interventions, problem solving (0.657; 35/67, 52%) and action planning (0.645; 31/67, 46%) had the highest weights. The remaining weights ranged from 0.477-0.553 (). Of the nine BCT categories coded in ≥50% of the interventions, category 5 natural consequences (0.553; 38/67, 57%) and category 9 comparison of outcomes (0.524; 63/67, 94%) were associated with the highest weights.
RoB and Completeness of Reporting
The overall RoB among the 45 RCTs ranged from some risk (4/45, 8%) to high risk (41/45, 91%). This was largely because of RoB in deviation from the intended intervention (7/45, 15% some risk; 38/45, 84% high risk) and measurement of the outcome (31/45, 68% high risk). Most studies had a low RoB for the remaining categories (n=34-44, depending on the category). Because of the risk of confounding, 95% (21/22) of the nonrandomized studies were found to have critical RoB. RoB in the measurement of outcome was moderate (10/67, 15%) or serious (9/67, 13%) for most single-arm studies, whereas it remained low across other categories (see Figure S4 infor RoB among the included studies [ - ]). If not for the lack of blinding, then only 58% of studies would have had a high overall high RoB, mainly because of bias in outcome measurement owing to the reliance on self-reported PA. Mean completeness of reporting was moderate, with 69.4% (71.4% for RCTs and 65.2% for nonrandomized studies) of applicable CONSORT-eHealth items covered in the included publications. Nearly one-third of the applicable items (mean of 30.6%, SD 9.4%) were not reported. For RCTs and nonrandomized studies, mean values of 15.5% (SD 3.4%) and 32.4% (SD 4.7%), respectively, of CONSORT-eHealth items (overall mean 20.8%, SD 8.8%) were not applicable on a case-by-case basis.
The purpose of this review was to provide a comprehensive, updated overview of eHealth intervention research designed to promote PA and to explore intervention characteristics (ie, duration, delivery modalities, use of theory, and BCTs) associated with increased PA levels. Many of the included studies were published after July 2018 and focused on feasibility, which indicates the rapidly growing yet early state of the field. Across the studies, there was substantial heterogeneity in the participants, interventions, and outcomes. All studies had high RoB for some domains, and incomplete reporting was problematic. Nevertheless, findings suggest that eHealth may be an effective strategy to enhance PA levels with selected modalities, BCTs, and behavioral theories that potentially enhance effectiveness.
Current State of the Literature
The growing number of published articles reporting on eHealth PA interventions for adults with cancer (48% of articles published since July 2018) aligns with several funding calls for eHealth research, institutional strategic priorities, and the growing prevalence of, and preference for, eHealth among adults with cancer [- ]. With the restrictions imposed by the COVID-19 pandemic on face-to-face PA programs, continued acceleration in this field is expected [ ]. The COVID-19 pandemic has highlighted the need for eHealth PA interventions in oncology, and such interventions will continue to remain relevant beyond the pandemic, especially for improving the reach of PA interventions to underserved populations with cancer (eg, remote or rural) [ , ]. For example, an ongoing study in Canada that aims to bring exercise oncology programs to remote and rural cancer populations has delivered all classes remotely during the COVID-19 pandemic and will continue to offer videoconference-based programs (NCT04478851) [ , ]. As many of the included studies tested the feasibility of using eHealth for PA promotion in adults with cancer (36%) using single-arm designs or smaller RCTs, the findings on the effectiveness to change PA levels remain largely preliminary. Next steps could include study designs, such as factorial RCTs or alternative trial designs with the capacity to quantify the contribution of intervention effectiveness from various technology components, theories, and BCTs. Finally, larger multisite RCTs or meta-analyses of comparable studies to strengthen the evidence for the effectiveness of these interventions will be required to continue to grow our knowledge [ - ].
Overall, this review highlights that eHealth interventions can increase PA levels, with 52% of the studies reporting significant increases in postintervention PA. Previous reviews have reported that 50%-80% of eHealth PA interventions for adults with cancer reported significant improvements in PA levels [- ]. Differences in these findings maybe because of the inclusion of studies that were underpowered to detect changes in PA levels (ie, feasibility trials and those aiming to impact a primary outcome other than PA levels), as well as intervention heterogeneity (ie, varied duration, delivery modalities, use of theory, and BCTs). Nevertheless, eHealth PA interventions have the potential to enhance PA levels, although optimization is required. The first step to optimization is to examine eHealth PA intervention components and their impact on effectiveness to change PA behavior.
Intervention Characteristics That May Promote PA Behavior Change
Findings from this review show that both well-established eHealth components (eg, informational websites) and emerging technologies (eg, mHealth) were associated with increased PA levels both when used alone or in combination with other eHealth. Researchers are encouraged to consider the pros and cons for each type of eHealth when designing eHealth PA interventions. For example, the pros of mHealth include the ability to deliver real-time, context-aware behavior change interventions; passively monitor PA; and relative ubiquity in developed countries (eg, nearly 90% of Canadians own a smartphone) [, , ]. Meanwhile, websites that have the highest weight of any eHealth component may be selected for their familiarity and ease of use among older adults [ ]. Moving forward, remaining flexible to align eHealth interventions with participant needs and preferences will likely be important [ , ].
A finding from this review that stands in contrast to those of previous reviews in exercise oncology is that a higher percentage of unsupervised interventions (56%; those without face-to-face interaction) were successful at increasing PA levels compared with those that were partially supervised (41%; those with one or more face-to-face components) [, ]. This may be because of feelings of autonomy promoted by unsupervised interventions, a factor that has been linked to increased intrinsic motivation and PA behavior change [ - ]. In addition, it may be in part because of the more frequent use of behavioral theories (unsupervised: 63%; supervised: 56%) and BCTs (unsupervised mean: 13.8; supervised mean: 11.8) in the included unsupervised interventions, which have been associated with effectiveness in web-based behavioral interventions [ ]. Direct comparisons of unsupervised and partially supervised eHealth PA interventions will be required to draw definitive conclusions on their relative effectiveness.
Recommendations have been made to use behavioral theories to guide intervention design to enhance the effectiveness of behavior change interventions [, ]. Common behavioral theories, such as social cognitive theory, the transtheoretical model, and the theory of planned behavior, have been used in roughly half of eHealth PA interventions for adults with cancer [ - ]. Although the weights for studies using social cognitive theory, the transtheoretical model, the theory of planned behavior, or multiple theories (0.565-0.750) were higher than of those using none at all (0.500), 50% of the interventions that were not theory based also resulted in significant increases in PA levels. Furthermore, it is possible that some articles may have drawn upon theoretically based intervention components without explicitly discussing the use of theory. These mixed results add to the ongoing debate on the role of behavioral theories in real-world interventions [ ]. Further examination of the use of theory (eg, theoretical integration and/or use of technology-specific models or theories) is needed to understand its impact, or lack thereof, in eHealth PA interventions.
The most commonly used BCTs in this review of eHealth PA interventions were goal setting and self-monitoring, which is similar to what has been reported in face-to-face PA interventions . However, more BCTs were used across studies in this review, for both mean number per study and overall variety, than in reviews assessing face-to-face interventions [ ]. Notably, current findings align with earlier research that has also suggested that certain BCTs may be more effective than others [ , , ]. Further research is needed to understand the use of BCTs (ie, types and combinations) and their potential impact on intervention effectiveness in eHealth PA research. Indeed, these weight analyses revealed that eHealth interventions with more BCTs were more likely to report significant improvements in PA levels.
RoB and Completeness of Reporting
Most reviewed studies (93%) had high overall RoB (ie, in one or more domains). This was, in large part, because of the lack of blinding. The inability to blind participants and researchers to PA interventions is a commonly reported limitation, irrespective of eHealth use [, ]. Consequently, if this domain were removed, then the RoB would remain high in only 58% of the studies, primarily because of the reliance on self-reported PA outcomes [ ]. Where possible, researchers may wish to integrate both objective and subjective PA measures into studies to reduce RoB [ ]. Objective PA assessment is increasingly accessible, given the activity trackers in mHealth (eg, phones) and decreasing costs. Finally, the finding that all included studies were incompletely reported is problematic. Researchers are urged to follow the reporting guidelines appropriate for their study design, which can be found on the web [ ].
There are important considerations to keep in mind when interpreting the findings. The broad inclusion criteria of the review, although selected intentionally to provide a comprehensive overview of this emerging field, hindered the ability to perform quantitative meta-analyses. Despite the systematic review, additional articles may have been missed if published in gray literature or in other languages. Although weight analyses were performed to provide insights for future research, their outputs must be interpreted with caution, as they are not a measure of statistical significance. Any reported associations remain purely exploratory and must be substantiated in future robust study designs. In addition, more than half of the included studies were underpowered to detect changes in PA as a secondary outcome, which is likely to bias weights toward the null. Some study characteristics in the weight analyses were represented in only a few studies, and most studies used complex interventions, making it difficult to identify the effect of individual components on outcomes. Finally, the authors did not complete BCT coder training before extraction, which may have led to some inaccuracies in BCT coding. However, efforts were made to minimize errors by double checking all codes and discussing with the senior author (NCR), an expert in PA behavior change, as needed.
Research Needs and Opportunities
Consolidating the evidence on eHealth PA interventions for adults with cancer led to the identification of several research needs and opportunities that remain to be addressed. First, only 9 studies featured follow-up assessments to track PA behavior change after intervention completion. Examining the long-term maintenance of PA is critical to determine whether these interventions can have a lasting impact on PA levels. Second, it will be important to explore whether completely unsupervised eHealth interventions or eHealth interventions with limited supervision can rival the effectiveness of face-to-face supervised PA programs to increase PA levels in adults with cancer. Such work is needed to advocate for eHealth use in this field and may be crucial to the implementation of scalable PA programs for adults with cancer. Third, examining the effectiveness of videoconferencing platforms, which have surged in popularity during the COVID-19 pandemic, is warranted. Videoconferencing has the potential to leverage the advantages of supervised interventions (eg, live tailored feedback, social interaction, and accountability) while remaining accessible . Fourth, given the rapidly evolving nature of eHealth, testing effectiveness using fully powered alternative trial designs (eg, SMART [sequential multiple assignment randomized trial], microrandomized trials, and factorial RCTs) is warranted so that evaluation can better match the pace of development, heighten external validity, and inform the translation of evidence to practice [ , ]. Such designs also allow researchers to establish definitive links between intervention components and changes in PA levels, allowing for systematic optimization of effectiveness. Finally, evaluations of cost-effectiveness are needed to inform real-world implementations of eHealth PA behavior change programs, as none were reported herein [ ].
This review summarizes findings from the rapidly growing field of eHealth PA interventions for adults affected by cancer. Although eHealth use in these interventions varies widely, the results are suggestive of positive outcomes. Furthermore, most studies integrated BCTs and relevant theories. Efforts are required to understand eHealth PA interventions better by exploring the impact on PA maintenance, investigating ways to optimize their effectiveness (by using BCTs, theories, and emerging technologies), and affirming effectiveness by applying well-powered alternative trial designs. Despite the early and evolving nature of this field, positive results suggest there is a case for integrating eHealth with efforts to promote PA, health, and well-being for adults affected by cancer.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors would like to thank Rosemary Twomey for her help with the PROSPERO registration and Alix Hayden for her assistance in developing the search strategy.
M Ester, MHM, MM, and SNCR conceived the study. M Ester and SNCR developed the protocol. M Ester performed the search and article processing. M Ester and M Eisele performed the article selection and data extraction. M Ester and M Eisele completed the data synthesis or analyses. M Ester, AW, and SNCR wrote the first draft of the manuscript. All authors reviewed, edited, and approved the final version of the manuscript.
Conflicts of Interest
Additional information on the review methodology and the included studies.DOCX File , 397 KB
- Stout NL, Baima J, Swisher AK, Winters-Stone KM, Welsh J. A systematic review of exercise systematic reviews in the cancer literature (2005-2017). PM R 2017 Sep;9(9S2):347-384 [FREE Full text] [CrossRef] [Medline]
- Campbell KL, Winters-Stone KM, Wiskemann J, May AM, Schwartz AL, Courneya KS, et al. Exercise guidelines for cancer survivors: consensus statement from international multidisciplinary roundtable. Med Sci Sports Exerc 2019 Nov;51(11):2375-2390. [CrossRef] [Medline]
- Schmitz KH, Courneya KS, Matthews C, Demark-Wahnefried W, Galvão DA, Pinto BM, American College of Sports Medicine. American College of Sports Medicine roundtable on exercise guidelines for cancer survivors. Med Sci Sports Exerc 2010 Jul;42(7):1409-1426. [CrossRef] [Medline]
- Mina DS, Sabiston CM, Au D, Fong AJ, Capozzi LC, Langelier D, et al. Connecting people with cancer to physical activity and exercise programs: a pathway to create accessibility and engagement. Curr Oncol 2018 Apr;25(2):149-162 [FREE Full text] [CrossRef] [Medline]
- Thraen-Borowski KM, Gennuso KP, Cadmus-Bertram L. Accelerometer-derived physical activity and sedentary time by cancer type in the United States. PLoS One 2017;12(8):e0182554 [FREE Full text] [CrossRef] [Medline]
- Sheeran P, Abraham C, Jones K, Villegas ME, Avishai A, Symes YR, et al. Promoting physical activity among cancer survivors: Meta-analysis and meta-CART analysis of randomized controlled trials. Health Psychol 2019 Jun;38(6):467-482. [CrossRef] [Medline]
- Clifford BK, Mizrahi D, Sandler CX, Barry BK, Simar D, Wakefield CE, et al. Barriers and facilitators of exercise experienced by cancer survivors: a mixed methods systematic review. Support Care Cancer 2018 Mar;26(3):685-700. [CrossRef] [Medline]
- Gurgel AR, Mingroni-Netto P, Farah JC, de Brito CM, Levin AS, Brum PC. Determinants of health and physical activity levels among breast cancer survivors during the COVID-19 pandemic: a cross-sectional study. Front Physiol 2021;12:624169 [FREE Full text] [CrossRef] [Medline]
- Jette AM. Mobile Technology: Increasing the reach and scalability of physical therapist services in the digital age. Phys Ther 2019 Feb 01;99(2):125-126. [CrossRef] [Medline]
- Mendoza-Vasconez AS, Linke S, Muñoz M, Pekmezi D, Ainsworth C, Cano M, et al. Promoting physical activity among underserved populations. Curr Sports Med Rep 2016;15(4):290-297 [FREE Full text] [CrossRef] [Medline]
- Lewis BA, Napolitano MA, Buman MP, Williams DM, Nigg CR. Future directions in physical activity intervention research: expanding our focus to sedentary behaviors, technology, and dissemination. J Behav Med 2017 Feb;40(1):112-126. [CrossRef] [Medline]
- Fareed N, Swoboda CM, Jonnalagadda P, Huerta TR. Persistent digital divide in health-related internet use among cancer survivors: findings from the Health Information National Trends Survey, 2003-2018. J Cancer Surviv 2021 Feb;15(1):87-98 [FREE Full text] [CrossRef] [Medline]
- Phillips SM, Conroy DE, Keadle SK, Pellegrini CA, Lloyd GR, Penedo FJ, et al. Breast cancer survivors' preferences for technology-supported exercise interventions. Support Care Cancer 2017 Dec;25(10):3243-3252. [CrossRef] [Medline]
- Alfano CM, Bluethmann SM, Tesauro G, Perna F, Agurs-Collins T, Elena JW, et al. NCI Funding trends and priorities in physical activity and energy balance research among cancer survivors. J Natl Cancer Inst 2016 Jan;108(1):djv285. [CrossRef] [Medline]
- Roberts AL, Fisher A, Smith L, Heinrich M, Potts HW. Digital health behaviour change interventions targeting physical activity and diet in cancer survivors: a systematic review and meta-analysis. J Cancer Surviv 2017 Dec;11(6):704-719 [FREE Full text] [CrossRef] [Medline]
- Haberlin C, O'Dwyer T, Mockler D, Moran J, O'Donnell DM, Broderick J. The use of eHealth to promote physical activity in cancer survivors: a systematic review. Support Care Cancer 2018 Jun 16:3323-3336. [CrossRef] [Medline]
- Schaffer K, Panneerselvam N, Loh KP, Herrmann R, Kleckner IR, Dunne RF, et al. Systematic review of randomized controlled trials of exercise interventions using digital activity trackers in patients with cancer. J Natl Compr Canc Netw 2019 Jan;17(1):57-63 [FREE Full text] [CrossRef] [Medline]
- Kiss N, Baguley BJ, Ball K, Daly RM, Fraser SF, Granger CL, et al. Technology-supported self-guided nutrition and physical activity interventions for adults with cancer: systematic review. JMIR Mhealth Uhealth 2019 Feb 12;7(2):e12281 [FREE Full text] [CrossRef] [Medline]
- Dorri S, Asadi F, Olfatbakhsh A, Kazemi A. A Systematic Review of Electronic Health (eHealth) interventions to improve physical activity in patients with breast cancer. Breast Cancer 2020 Jan;27(1):25-46. [CrossRef] [Medline]
- Grimmett C, Corbett T, Brunet J, Shepherd J, Pinto BM, May CR, et al. Systematic review and meta-analysis of maintenance of physical activity behaviour change in cancer survivors. Int J Behav Nutr Phys Act 2019 Apr 27;16(1):37 [FREE Full text] [CrossRef] [Medline]
- Howlett N, Trivedi D, Troop NA, Chater AM. Are physical activity interventions for healthy inactive adults effective in promoting behavior change and maintenance, and which behavior change techniques are effective? A systematic review and meta-analysis. Transl Behav Med 2018 Feb 28:147-157. [CrossRef] [Medline]
- Bluethmann SM, Bartholomew LK, Murphy CC, Vernon SW. Use of theory in behavior change interventions. Health Educ Behav 2017 Apr;44(2):245-253 [FREE Full text] [CrossRef] [Medline]
- Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009 Jul 21;6(7):e1000097 [FREE Full text] [CrossRef] [Medline]
- Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev 2016 Dec 05;5(1):210 [FREE Full text] [CrossRef] [Medline]
- Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, 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 2013 Aug;46(1):81-95. [CrossRef] [Medline]
- Sterne JA, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. Br Med J 2019 Aug 28;366:l4898. [CrossRef] [Medline]
- Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. Br Med J 2016 Oct 12;355:i4919 [FREE Full text] [CrossRef] [Medline]
- Eysenbach G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of web-based and mobile health interventions. J Med Internet Res 2011;13(4):e126 [FREE Full text] [CrossRef] [Medline]
- Rodgers M, Sowden A, Petticrew M, Arai L, Roberts H, Britten N, et al. Testing methodological guidance on the conduct of narrative synthesis in systematic reviews. Evaluation 2009 Jan 01;15(1):49-73. [CrossRef]
- Ogilvie D, Fayter D, Petticrew M, Sowden A, Thomas S, Whitehead M, et al. The harvest plot: a method for synthesising evidence about the differential effects of interventions. BMC Med Res Methodol 2008 Feb 25;8:8 [FREE Full text] [CrossRef] [Medline]
- Williams M, Rana N, Dwivedi Y. The unified theory of acceptance and use of technology (UTAUT): a literature review. J Enterp Inf Manag 2015;28(3):443-488. [CrossRef]
- Mayo NE, Moriello C, Scott SC, Dawes D, Auais M, Chasen M. Pedometer-facilitated walking intervention shows promising effectiveness for reducing cancer fatigue: a pilot randomized trial. Clin Rehabil 2014 Dec;28(12):1198-1209. [CrossRef] [Medline]
- Maxwell-Smith C, Hince D, Cohen PA, Bulsara MK, Boyle T, Platell C, et al. A randomized controlled trial of WATAAP to promote physical activity in colorectal and endometrial cancer survivors. Psychooncology 2019 Jul;28(7):1420-1429. [CrossRef] [Medline]
- Park S, Kim K, Ahn HK, Kim JW, Min G, Chung BH, et al. Impact of lifestyle intervention for patients with prostate cancer. Am J Health Behav 2020 Jan 01;44(1):90-99. [CrossRef] [Medline]
- Gomersall SR, Skinner TL, Winkler E, Healy GN, Eakin E, Fjeldsoe B. Feasibility, acceptability and efficacy of a text message-enhanced clinical exercise rehabilitation intervention for increasing 'whole-of-day' activity in people living with and beyond cancer. BMC Public Health 2019 Jun 03;19(Suppl 2):542 [FREE Full text] [CrossRef] [Medline]
- Gehring K, Kloek CJ, Aaronson NK, Janssen KW, Jones LW, Sitskoorn MM, 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 2018 Mar;32(3):352-366 [FREE Full text] [CrossRef] [Medline]
- Singh B, Spence RR, Sandler CX, Tanner J, Hayes SC. Feasibility and effect of a physical activity counselling session with or without provision of an activity tracker on maintenance of physical activity in women with breast cancer - A randomised controlled trial. J Sci Med Sport 2020 Mar;23(3):283-290. [CrossRef] [Medline]
- Buscemi J, Oswald LB, Baik SH, Buitrago D, Iacobelli F, Phillips SM, et al. My health smartphone intervention decreases daily fat sources among Latina breast cancer survivors. J Behav Med 2020 Oct;43(5):732-742. [CrossRef] [Medline]
- Chapman J, Fletcher C, Flight I, Wilson C. Pilot randomized trial of a volitional help sheet-based tool to increase leisure time physical activity in breast cancer survivors. Br J Health Psychol 2018 Sep;23(3):723-740. [CrossRef] [Medline]
- Fazzino TL, Fabian C, Befort CA. Change in physical activity during a weight management intervention for breast cancer survivors: association with weight outcomes. Obesity (Silver Spring) 2017 Nov;25 Suppl 2:109-115 [FREE Full text] [CrossRef] [Medline]
- Hartman SJ, Nelson SH, Weiner LS. Patterns of fitbit use and activity levels throughout a physical activity intervention: exploratory analysis from a randomized controlled trial. JMIR Mhealth Uhealth 2018 Feb 05;6(2):e29 [FREE Full text] [CrossRef] [Medline]
- Hatchett A, Hallam JS, Ford MA. Evaluation of a social cognitive theory-based email intervention designed to influence the physical activity of survivors of breast cancer. Psychooncology 2013 Apr;22(4):829-836. [CrossRef] [Medline]
- Lynch BM, Nguyen NH, Moore MM, Reeves MM, Rosenberg DE, Boyle T, 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 2019 Aug 15;125(16):2846-2855. [CrossRef] [Medline]
- Lynch BM, Nguyen NH, Moore MM, Reeves MM, Rosenberg DE, Boyle T, et al. Maintenance of physical activity and sedentary behavior change, and physical activity and sedentary behavior change after an abridged intervention: Secondary outcomes from the ACTIVATE Trial. Cancer 2019 Aug 15;125(16):2856-2860 [FREE Full text] [CrossRef] [Medline]
- McNeil J, Brenner DR, Stone CR, O'Reilly R, Ruan Y, Vallance JK, et al. Activity tracker to prescribe various exercise intensities in breast cancer survivors. Med Sci Sports Exerc 2019 May;51(5):930-940. [CrossRef] [Medline]
- Park S, Lee I, Kim JI, Park H, Lee JD, Uhm KE, et al. Factors associated with physical activity of breast cancer patients participating in exercise intervention. Support Care Cancer 2019 May;27(5):1747-1754. [CrossRef] [Medline]
- Paxton RJ, Hajek R, Newcomb P, Dobhal M, Borra S, Taylor WC, et al. A lifestyle intervention via email in minority breast cancer survivors: randomized parallel-group feasibility study. JMIR Cancer 2017 Sep 21;3(2):e13 [FREE Full text] [CrossRef] [Medline]
- 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 2018 Jun 07;7(6):140 [FREE Full text] [CrossRef] [Medline]
- Short CE, Rebar A, James EL, Duncan MJ, Courneya KS, Plotnikoff RC, et al. How do different delivery schedules of tailored web-based physical activity advice for breast cancer survivors influence intervention use and efficacy? J Cancer Surviv 2017 Feb;11(1):80-91. [CrossRef] [Medline]
- Uhm KE, Yoo JS, Chung SH, Lee JD, Lee I, Kim JI, 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 2017 Feb;161(3):443-452. [CrossRef] [Medline]
- Weiner LS, Takemoto M, Godbole S, Nelson SH, Natarajan L, Sears DD, et al. Breast cancer survivors reduce accelerometer-measured sedentary time in an exercise intervention. J Cancer Surviv 2019 Jun;13(3):468-476 [FREE Full text] [CrossRef] [Medline]
- Allicock M, Kendzor D, Sedory A, Gabriel KP, Swartz MD, Thomas P, et al. A pilot and feasibility mobile health intervention to support healthy behaviors in African American breast cancer survivors. J Racial Ethn Health Disparities 2021 Feb;8(1):157-165. [CrossRef] [Medline]
- Gokal K, Wallis D, Ahmed S, Boiangiu I, Kancherla K, Munir F. Effects of a self-managed home-based walking intervention on psychosocial health outcomes for breast cancer patients receiving chemotherapy: a randomised controlled trial. Support Care Cancer 2016 Mar;24(3):1139-1166. [CrossRef] [Medline]
- Van Blarigan EL, Chan H, Van Loon K, Kenfield SA, Chan JM, Mitchell E, 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 2019 Mar 11;19(1):218 [FREE Full text] [CrossRef] [Medline]
- Haggerty AF, Hagemann A, Barnett M, Thornquist M, Neuhouser ML, Horowitz N, et al. A randomized, controlled, multicenter study of technology-based weight loss interventions among endometrial cancer survivors. Obesity (Silver Spring) 2017 Nov;25 Suppl 2:102-108 [FREE Full text] [CrossRef] [Medline]
- Chow EJ, Doody DR, Di C, Armenian SH, Baker KS, Bricker JB, 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 2020 Oct 10:554-563. [CrossRef] [Medline]
- Edbrooke L, Granger CL, Clark RA, Denehy L. Physical activity levels are low in inoperable lung cancer: exploratory analyses from a randomised controlled trial. J Clin Med 2019 Aug 23;8(9):1288 [FREE Full text] [CrossRef] [Medline]
- Cox M, Basen-Engquist K, Carmack CL, Blalock J, Li Y, Murray J, et al. Comparison of internet and telephone interventions for weight loss among cancer survivors: randomized controlled trial and feasibility study. JMIR Cancer 2017 Sep 27;3(2):e16 [FREE Full text] [CrossRef] [Medline]
- Forbes CC, Blanchard CM, Mummery WK, Courneya KS. Feasibility and preliminary efficacy of an online intervention to increase physical activity in nova scotian cancer survivors: a randomized controlled trial. JMIR Cancer 2015 Nov 23;1(2):e12 [FREE Full text] [CrossRef] [Medline]
- Golsteijn RH, 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 2018 Oct 30;15(1):106 [FREE Full text] [CrossRef] [Medline]
- Ormel HL, van der Schoot GG, Westerink NL, Sluiter WJ, Gietema JA, Walenkamp AM. Self-monitoring physical activity with a smartphone application in cancer patients: a randomized feasibility study (SMART-trial). Support Care Cancer 2018 May 21:3915-3923. [CrossRef] [Medline]
- Webb J, Fife-Schaw C, Ogden J. A randomised control trial and cost-consequence analysis to examine the effects of a print-based intervention supported by internet tools on the physical activity of UK cancer survivors. Public Health 2019 Jun;171:106-115 [FREE Full text] [CrossRef] [Medline]
- Webb J, Peel J, Fife-Schaw C, Ogden J. A mixed methods process evaluation of a print-based intervention supported by internet tools to improve physical activity in UK cancer survivors. Public Health 2019 Oct;175:19-27 [FREE Full text] [CrossRef] [Medline]
- Bantum EO, Albright CL, White KK, Berenberg JL, Layi G, Ritter PL, et al. Surviving and thriving with cancer using a Web-based health behavior change intervention: randomized controlled trial. J Med Internet Res 2014;16(2):e54 [FREE Full text] [CrossRef] [Medline]
- 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 2018 Sep 21;15(10):2081 [FREE Full text] [CrossRef] [Medline]
- Frensham LJ, Parfitt G, Dollman J. Predicting engagement with online walking promotion among metropolitan and rural cancer survivors. Cancer Nurs 2020;43(1):52-59. [CrossRef] [Medline]
- Gell NM, Grover KW, Savard L, Dittus K. Outcomes of a text message, Fitbit, and coaching intervention on physical activity maintenance among cancer survivors: a randomized control pilot trial. J Cancer Surviv 2020 Feb;14(1):80-88. [CrossRef] [Medline]
- Kanera IM, Bolman CA, Willems RA, Mesters I, Lechner L. Lifestyle-related effects of the web-based Kanker Nazorg Wijzer (Cancer Aftercare Guide) intervention for cancer survivors: a randomized controlled trial. J Cancer Surviv 2016 Mar 17:883-897. [CrossRef] [Medline]
- Kanera IM, Willems RA, Bolman CA, Mesters I, Verboon P, Lechner L. Long-term effects of a web-based cancer aftercare intervention on moderate physical activity and vegetable consumption among early cancer survivors: a randomized controlled trial. Int J Behav Nutr Phys Act 2017 Feb 10;14(1):19 [FREE Full text] [CrossRef] [Medline]
- Mayer DK, Landucci G, Awoyinka L, Atwood AK, Carmack CL, Demark-Wahnefried W, et al. SurvivorCHESS to increase physical activity in colon cancer survivors: can we get them moving? J Cancer Surviv 2018 Feb;12(1):82-94. [CrossRef] [Medline]
- Park J, Lee J, Oh M, Park H, Chae J, Kim D, et al. The effect of oncologists' exercise recommendations on the level of exercise and quality of life in survivors of breast and colorectal cancer: A randomized controlled trial. Cancer 2015 Aug 15;121(16):2740-2748 [FREE Full text] [CrossRef] [Medline]
- Valle CG, Tate DF, Mayer DK, Allicock M, Cai J. A randomized trial of a Facebook-based physical activity intervention for young adult cancer survivors. J Cancer Surviv 2013 Sep;7(3):355-368 [FREE Full text] [CrossRef] [Medline]
- Rabin C, Dunsiger S, Ness KK, Marcus BH. Internet-based physical activity intervention targeting young adult cancer survivors. J Adolesc Young Adult Oncol 2011 Dec;1(4):188-194 [FREE Full text] [CrossRef] [Medline]
- Robertson MC, Lyons EJ, Liao Y, Baum ML, Basen-Engquist KM. Gamified text messaging contingent on device-measured steps: randomized feasibility study of a physical activity intervention for cancer survivors. JMIR Mhealth Uhealth 2020 Nov 24;8(11):e18364 [FREE Full text] [CrossRef] [Medline]
- Yun YH, Lim CI, Lee ES, Kim YT, Shin KH, Kim Y, et al. Efficacy of health coaching and a web-based program on physical activity, weight, and distress management among cancer survivors: A multi-centered randomised controlled trial. Psychooncology 2020 Jul;29(7):1105-1114. [CrossRef] [Medline]
- Shang J, Wenzel J, Krumm S, Griffith K, Stewart K. Who will drop out and who will drop in: exercise adherence in a randomized clinical trial among patients receiving active cancer treatment. Cancer Nurs 2012;35(4):312-322 [FREE Full text] [CrossRef] [Medline]
- Villaron C, Cury F, Eisinger F, Cappiello M, Marqueste T. Telehealth applied to physical activity during cancer treatment: a feasibility, acceptability, and randomized pilot study. Support Care Cancer 2018 Oct;26(10):3413-3421. [CrossRef] [Medline]
- Chan JM, Van Blarigan EL, Langlais CS, Zhao S, Ramsdill JW, Daniel K, 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 2020 Dec 31;22(12):e19238 [FREE Full text] [CrossRef] [Medline]
- Kenfield SA, Van Blarigan EL, Ameli N, Lavaki E, Cedars B, Paciorek AT, 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 2019 Jan 09:950-958. [CrossRef] [Medline]
- Alibhai SM, Mina DS, Ritvo P, Tomlinson G, Sabiston C, Krahn M, et al. A phase II randomized controlled trial of three exercise delivery methods in men with prostate cancer on androgen deprivation therapy. BMC Cancer 2019 Jan 03;19(1):2 [FREE Full text] [CrossRef] [Medline]
- Bade BC, Hyer JM, Bevill BT, Pastis A, Rojewski AM, Toll BA, et al. A patient-centered activity regimen improves participation in physical activity interventions in advanced-stage lung cancer. Integr Cancer Ther 2018 Sep;17(3):921-927 [FREE Full text] [CrossRef] [Medline]
- Naito T, Mitsunaga S, Miura S, Tatematsu N, Inano T, Mouri T, et al. Feasibility of early multimodal interventions for elderly patients with advanced pancreatic and non-small-cell lung cancer. J Cachexia Sarcopenia Muscle 2019 Feb;10(1):73-83 [FREE Full text] [CrossRef] [Medline]
- Befort CA, Klemp JR, Austin HL, Perri MG, Schmitz KH, Sullivan DK, et al. Outcomes of a weight loss intervention among rural breast cancer survivors. Breast Cancer Res Treat 2012 Apr;132(2):631-639 [FREE Full text] [CrossRef] [Medline]
- Nápoles AM, Santoyo-Olsson J, Chacón L, Stewart AL, Dixit N, Ortiz C. Feasibility of a mobile phone app and telephone coaching survivorship care planning program among spanish-speaking breast cancer survivors. JMIR Cancer 2019 Jul 09;5(2):e13543 [FREE Full text] [CrossRef] [Medline]
- Pope Z, Lee JE, Zeng N, Lee HY, Gao Z. Feasibility of smartphone application and social media intervention on breast cancer survivors' health outcomes. Transl Behav Med 2018 Feb 17:11-22. [CrossRef] [Medline]
- Spark LC, Fjeldsoe BS, Eakin EG, Reeves MM. Efficacy of a text message-delivered extended contact intervention on maintenance of weight loss, physical activity, and dietary behavior change. JMIR Mhealth Uhealth 2015;3(3):e88 [FREE Full text] [CrossRef] [Medline]
- Wilson DB, Porter JS, Parker G, Kilpatrick J. Anthropometric changes using a walking intervention in African American breast cancer survivors: a pilot study. Prev Chronic Dis 2005 Apr;2(2):A16 [FREE Full text] [Medline]
- Chung IY, Jung M, Park YR, Cho D, Chung H, Min YH, et al. Exercise promotion and distress reduction using a mobile app-based community in breast cancer survivors. Front Oncol 2019;9:1505 [FREE Full text] [CrossRef] [Medline]
- Nyrop KA, Deal AM, Choi SK, Wagoner CW, Lee JT, Wood WA, et al. Measuring and understanding adherence in a home-based exercise intervention during chemotherapy for early breast cancer. Breast Cancer Res Treat 2018 Feb;168(1):43-55. [CrossRef] [Medline]
- Cairo J, Williams L, Bray L, Goetzke K, Perez AC. Evaluation of a mobile health intervention to improve wellness outcomes for breast cancer survivors. J Patient Cent Res Rev 2020;7(4):313-322 [FREE Full text] [CrossRef] [Medline]
- Cheong IY, An SY, Cha WC, Rha MY, Kim ST, Chang DK, et al. Efficacy of mobile health care application and wearable device in improvement of physical performance in colorectal cancer patients undergoing chemotherapy. Clin Colorectal Cancer 2018 Jun;17(2):353-362. [CrossRef] [Medline]
- Groen WG, Kuijpers W, Oldenburg HS, Wouters MW, Aaronson NK, van Harten WH. Supporting lung cancer patients with an interactive patient portal: feasibility study. JMIR Cancer 2017 Aug 08;3(2):e10 [FREE Full text] [CrossRef] [Medline]
- Hong YA, Goldberg D, Ory MG, Towne SD, Forjuoh SN, Kellstedt D, et al. Efficacy of a mobile-enabled web app (iCanFit) in promoting physical activity among older cancer survivors: a pilot study. JMIR Cancer 2015 Jun 26;1(1):e7 [FREE Full text] [CrossRef] [Medline]
- McCarroll ML, Armbruster S, Pohle-Krauza RJ, Lyzen AM, Min S, Nash DW, et al. Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application. Gynecol Oncol 2015 Jun;137(3):508-515. [CrossRef] [Medline]
- MacDonald AM, Chafranskaia A, Lopez CJ, Maganti M, Bernstein LJ, Chang E, et al. CaRE @ Home: Pilot study of an online multidimensional cancer rehabilitation and exercise program for cancer survivors. J Clin Med 2020 Sep 25;9(10):3092 [FREE Full text] [CrossRef] [Medline]
- Gell NM, Grover KW, Humble M, Sexton M, Dittus K. Efficacy, feasibility, and acceptability of a novel technology-based intervention to support physical activity in cancer survivors. Support Care Cancer 2017 Apr;25(4):1291-1300. [CrossRef] [Medline]
- Puszkiewicz P, Roberts AL, Smith L, Wardle J, Fisher A. Assessment of cancer survivors' experiences of using a publicly available physical activity mobile application. JMIR Cancer 2016 May 31;2(1):e7 [FREE Full text] [CrossRef] [Medline]
- Short CE, Finlay A, Sanders I, Maher C. Development and pilot evaluation of a clinic-based mHealth app referral service to support adult cancer survivors increase their participation in physical activity using publicly available mobile apps. BMC Health Serv Res 2018 Dec 16;18(1):27 [FREE Full text] [CrossRef] [Medline]
- Abbott L, Hooke MC. Energy through motion: An activity intervention for cancer-related fatigue in an ambulatory infusion center. Clin J Oncol Nurs 2017 Oct 01;21(5):618-626. [CrossRef] [Medline]
- Javaheri PA, Nekolaichuk C, Haennel R, Parliament MB, McNeely ML. Feasibility of a pedometer-based walking program for survivors of breast and head and neck cancer undergoing radiation therapy. Physiother Can 2015;67(2):205-213 [FREE Full text] [CrossRef] [Medline]
- Zhang X, McClean D, Ko E, Morgan MA, Schmitz K. Exercise among women with ovarian cancer: a feasibility and pre-/post-test exploratory pilot study. Oncol Nurs Forum 2017 May 01;44(3):366-374. [CrossRef] [Medline]
- Trinh L, Arbour-Nicitopoulos KP, Sabiston CM, Berry SR, Loblaw A, Alibhai SM, et al. RiseTx: testing the feasibility of a web application for reducing sedentary behavior among prostate cancer survivors receiving androgen deprivation therapy. Int J Behav Nutr Phys Act 2018 Jun 07;15(1):49. [CrossRef] [Medline]
- Bandura A. Health promotion by social cognitive means. Health Educ Behav 2004 Apr;31(2):143-164. [CrossRef] [Medline]
- Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot 1997;12(1):38-48. [CrossRef] [Medline]
- Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Proc 1991 Dec;50(2):179-211. [CrossRef]
- Shephard R. Godin leisure-time exercise questionnaire. Med Sci Sports Exerc 1997;29(Supplement):36-38. [CrossRef]
- Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003 Aug;35(8):1381-1395. [CrossRef] [Medline]
- Bland KA, Bigaran A, Campbell KL, Trevaskis M, Zopf EM. Exercising in isolation? The role of telehealth in exercise oncology during the COVID-19 pandemic and beyond. Phys Ther 2020 Sep 28;100(10):1713-1716 [FREE Full text] [CrossRef] [Medline]
- Culos-Reed S, Keats M, McNeely M, Cuthbert C, Santa MD, Campbell K, et al. Dissemination, implementation, and effectiveness of the exercise oncology survivorship partnership model: reaching rural cancer survivors to enhance quality of life. Canadian Cancer Society - CCS/CIHR Cancer Survivorship Team Grant. URL: https://webapps.cihr-irsc.gc.ca/cris/detail_e?pResearchId=9278449&p_version=CRIS&p_language=E&p_session_id=2307811 [accessed 2021-08-22]
- Ester M, McNeely ML, McDonough MH, Dreger J, Culos-Reed SN. Protocol: A cluster randomized controlled trial of a mobile application to support physical activity maintenance after an exercise oncology program. Contemp Clin Trials 2021 Jun 05;107:106474. [CrossRef] [Medline]
- Campbell M, Fitzpatrick R, Haines A, Kinmonth AL, Sandercock P, Spiegelhalter D, et al. Framework for design and evaluation of complex interventions to improve health. Br Med J 2000 Sep 16;321(7262):694-696 [FREE Full text] [CrossRef] [Medline]
- Almirall D, Nahum-Shani I, Sherwood NE, Murphy SA. Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Transl Behav Med 2014 Sep;4(3):260-274 [FREE Full text] [CrossRef] [Medline]
- Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychol 2015 Dec;34 Suppl:1220-1228. [CrossRef] [Medline]
- Hardeman W, Houghton J, Lane K, Jones A, Naughton F. A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity. Int J Behav Nutr Phys Act 2019 Apr 03;16(1):31 [FREE Full text] [CrossRef] [Medline]
- Table 22-10-0115-01: Smartphone use and smartphone habits by gender and age group. Statistics Canada. 2021. URL: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=2210011501 [accessed 2021-08-23]
- Czaja SJ, Sharit J, Lee CC, Nair SN, Hernández MA, Arana N, et al. Factors influencing use of an e-health website in a community sample of older adults. J Am Med Inform Assoc 2013;20(2):277-284 [FREE Full text] [CrossRef] [Medline]
- Furness K, Sarkies MN, Huggins CE, Croagh D, Haines TP. Impact of the method of delivering electronic health behavior change interventions in survivors of cancer on engagement, health behaviors, and health outcomes: systematic review and meta-analysis. J Med Internet Res 2020 Jun 23;22(6):e16112 [FREE Full text] [CrossRef] [Medline]
- Jonkman NH, van Schooten KS, Maier AB, Pijnappels M. eHealth interventions to promote objectively measured physical activity in community-dwelling older people. Maturitas 2018 Jul;113:32-39. [CrossRef] [Medline]
- Sweegers MG, Altenburg TM, Chinapaw MJ, Kalter J, Verdonck-de Leeuw IM, Courneya KS, et al. Which exercise prescriptions improve quality of life and physical function in patients with cancer during and following treatment? A systematic review and meta-analysis of randomised controlled trials. Br J Sports Med 2018 Apr;52(8):505-513. [CrossRef] [Medline]
- Ballin M, Hult A, Björk S, Lundberg E, Nordström P, Nordström A. Web-based exercise versus supervised exercise for decreasing visceral adipose tissue in older adults with central obesity: a randomized controlled trial. BMC Geriatr 2020 May 12;20(1):173 [FREE Full text] [CrossRef] [Medline]
- Mehra S, Dadema T, Kröse BJ, Visser B, Engelbert RH, Van Den Helder J, et al. Attitudes of older adults in a group-based exercise program toward a blended intervention; a focus-group study. Front Psychol 2016;7:1827 [FREE Full text] [CrossRef] [Medline]
- Teixeira PJ, Carraça EV, Markland D, Silva MN, Ryan RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act 2012;9:78 [FREE Full text] [CrossRef] [Medline]
- Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010;12(1):e4 [FREE Full text] [CrossRef] [Medline]
- Hagger MS, Weed M. DEBATE: Do interventions based on behavioral theory work in the real world? Int J Behav Nutr Phys Act 2019 Dec 25;16(1):36 [FREE Full text] [CrossRef] [Medline]
- White M, Dorman SM. Receiving social support online: implications for health education. Health Educ Res 2001 Dec;16(6):693-707. [CrossRef] [Medline]
- Finne E, Glausch M, Exner A, Sauzet O, Stölzel F, Seidel N. Behavior change techniques for increasing physical activity in cancer survivors: a systematic review and meta-analysis of randomized controlled trials. Cancer Manag Res 2018;10:5125-5143 [FREE Full text] [CrossRef] [Medline]
- Turner RR, Steed L, Quirk H, Greasley RU, Saxton JM, Taylor SJ, et al. Interventions for promoting habitual exercise in people living with and beyond cancer. Cochrane Database Syst Rev 2018 Sep 19;9:CD010192 [FREE Full text] [CrossRef] [Medline]
- Peddle-McIntyre CJ, Cavalheri V, Boyle T, McVeigh JA, Jeffery E, Lynch BM, et al. A review of accelerometer-based activity monitoring in cancer survivorship research. Med Sci Sports Exerc 2018 Sep;50(9):1790-1801. [CrossRef] [Medline]
- The EQUATOR network. The UK EQUATOR Centre. URL: https://www.equator-network.org/ [accessed 2021-08-23]
- Afshin A, Babalola D, Mclean M, Yu Z, Ma W, Chen C, et al. Information Technology and Lifestyle: A systematic evaluation of internet and mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use. J Am Heart Assoc 2016 Aug 31;5(9):e003058 [FREE Full text] [CrossRef] [Medline]
|BCT: behavior change technique|
|CONSORT: Consolidated Standards of Reporting Trials|
|mHealth: mobile health|
|PA: physical activity|
|PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses|
|PROSPERO: International Prospective Register of Systematic Reviews|
|RCT: randomized controlled trial|
|RoB: risk of bias|
|ROBINS-I: risk of bias in nonrandomized studies of interventions|
|SMART: sequential multiple assignment randomized trial|
Edited by D Vollmer Dahlke; submitted 16.03.21; peer-reviewed by C Lopez, L Voss; comments to author 22.05.21; revised version received 29.06.21; accepted 26.07.21; published 20.09.21Copyright
©Manuel Ester, Maximilian Eisele, Amanda Wurz, Meghan H McDonough, Margaret McNeely, S Nicole Culos-Reed. Originally published in JMIR Cancer (https://cancer.jmir.org), 20.09.2021.
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 JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.