Physical health outcomes and step cadence improvements: a digital physical activity intervention in cancer survivors.

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Introduction

An increasing number of people are living with and beyond cancer. 1 By 2026, an estimated 20.3 million cancer survivors will be living in the United States. 1 Many survivors experience long-term symptoms and comorbidities related to the disease and its treatments. 2 The cardiotoxic effects of chemotherapy, as well as sarcopenia or cancer cachexia, accelerate aging and age-related declines in certain measures of physical function, such as walking cadence or sit-to-stand performance. Additionally, cancer and its treatments can result in ongoing pain, which can interfere with activities of daily living and long-term treatment adherence. A strong body of evidence demonstrates that physical activity is safe and effective for cancer survivors in managing treatment-related side effects and improving overall health 3 and may improve survival. 4 Despite these benefits and strong recommendations from multiple health authorities, 5 , 6 including the American Cancer Society, 3 physical activity participation among survivors remains low. It is estimated that at least 75% of cancer survivors do not meet physical activity guidelines of engaging in 150-300 minutes per week of moderate-intensity aerobic activity or 75-150 minutes per week of vigorous-intensity activity. 7

Physical activity interventions are essential for promoting moderate-to-vigorous physical activity among cancer survivors. Most existing programs are short term (eg, 12-16 weeks) and resource intensive, which can limit their long-term sustainability and scalability. 8-10 Because most programs are not integrated into long-term cohort studies, the potential for tracking outcomes over time remains largely unexplored, and access to these programs is often constrained by geographic and financial barriers. Furthermore, the efficacy of many interventions has been demonstrated primarily in highly controlled research settings, leaving uncertainty as to whether these effects can be sustained when implemented under real-world conditions. To overcome these challenges and expand reach, digital, home-based programs can offer a promising alternative and are supported in recent literature. 9 , 11 , 12 Therefore, this study examined the effect of a 12-month multicomponent digital intervention grounded in the social cognitive theory 13 on measures of physical health in people living with and beyond cancer.

Methods

Study design and participants

This study used a 2-arm trial within a cohort design, as participants were enrolled from the American Cancer Society’s Cancer Prevention Study–3 (CPS-3) prospective cohort study. 14 , 15 Between 2006 and 2013, a total of 303 000 adults aged 30-65 years, without a history of cancer, were recruited into CPS-3 from various community locations across the United States (eg, Relay for Life, religious centers). At baseline, in 2015, and every 3 years thereafter, participants completed surveys including lifestyle and medical history and were followed for cancer incidence and mortality via linkage with state cancer registries and the National Death Index.

Details of the intervention have been previously described. 16 , 17 In 2022, a total of 2092 CPS-3 participants were invited to participate via email if they had been diagnosed with a cancer type associated with physical inactivity (ie, bladder, breast, colon, endometrial, kidney, rectal cancers, or multiple myeloma). Those interested took an eligibility survey to assess walking ability, current moderate-to-vigorous physical activity, last cancer treatment, and participation and readiness for exercise questionnaire 18 items. Participants were eligible to participate in the 2-arm trial if they (1) could walk without the use of a cane or walker, (2) had not undergone active cancer treatment within the past 6 months, (3) had not been instructed by a physician to avoid physical activity, (4) did not self-report exceeding physical activity guidelines (150 minutes per week of moderate-to-vigorous physical activity), or (5) owned a smartphone or tablet to enable use of the mobile Fitbit application. In total, 653 completed the eligibility survey, and eligible participants ( n = 446) consented electronically.

Participants who returned complete baseline measurements (eg, 7-day accelerometer wear and survey completion) were randomly assigned 2:1 to the intervention or control group using the randomizer function in R 19 (415 returned complete baseline materials within 1.5 months; 286 intervention group and 129 control group; Figure S1 ). After completing the baseline assessment, all participants (regardless of group assignment) received a Fitbit Charge 5 and were encouraged to wear it on their nondominant wrist 24-hours per day throughout the 12-month study period. Participants were invited to attend an optional 1-hour group online familiarization session with study staff to introduce their respective website and Fitbit syncing process. Although Fitbit served primarily as a self-monitoring tool, step count and cadence data were also collected following baseline assessment. The 12-month intervention was conducted from July 2022 to July 2023. Study measurements were completed via an online portal at 4 time points: baseline, 3 months, 6 months, and 12 months. All participants will remain in the cohort study where they will continue to receive annual online surveys for continued follow-up. All study procedures were approved by the Emory University institutional review board and registered on ClinicalTrials.gov ( NCT05356988 ).

Intervention

Designed for inactive cancer survivors using the social cognitive theory, 20 the intervention consisted of an interactive website promoting moderate-to-vigorous physical activity paired with a Fitbit Charge 5. Participants were instructed to visit the website weekly throughout the duration of the intervention. The intervention website included (1) an “active minutes” leaderboard where participants saw their rankings compared with 19 other participants with similar Fitbit activity levels; (2) asynchronous virtual exercise classes; (3) personalized goal-setting tools (targeting Fitbit active minutes and resistance training); (4) profile badges for meeting goals; (5) a community discussion board; (6) articles and blog posts featuring cancer survivor success stories, exercise recommendations, benefits of active living, and so forth; and (7) a personal journal with writing prompts. Biweekly updates (every other week) were made to progress the exercises and refresh other written content. Website usage was monitored by the number of “clicks.” Compliance was defined as equivalent to visiting the website at least once per week for at least 80% of the study. Following study completion, participants received a maximum possible compensation of $100 based on the number of surveys and accelerometers returned (not based on intervention adherence) and kept the Fitbit.

Control group

The control group received a static website with information and videos for improving balance and flexibility. Website user data were not collected for the control group, as the control website had limited engagement opportunities.

Outcome measures

Physical function outcomes included total steps per day and daily steps cadence (peak 1 minute and peak 30 minutes) measured from the Fitbit; sit-to-stand repetitions measured by the participant; and self-reported measures of mobility, walk pace, and average pain.

Total steps per day and daily steps cadence

Total steps per day, daily peak 1-minute cadence, and daily peak 30-minute cadence were assessed via Fitbit and collected via Fitabase (Small Steps Lab, LLC, San Diego, CA, United States). Higher step volume (total steps per day) and step rate (step cadence) have been shown to be associated with lower all-cause mortality and cancer mortality 21 , 22 and have been used as a validated marker of physical function. 23 Days with 23 hours or more of sedentary time or less than 150 steps per day (Fitbit) were excluded. Total steps per day were calculated as rolling 2-week averages across the duration of the study period. Peak 1-minute cadence was reported as the highest single minute of steps recorded per day. 24 Peak 30-minute cadence calculated as the average steps per minute for the highest consecutive or nonconsecutive 30 minutes in a day. 24 Total steps per day and daily peak cadence are valid, reliable measures that have been shown to decline with ageing and may also predict early mortality. 24-26 Because of the use of rolling 2-week averages and the absence of a true preintervention baseline (collected after random assignment) and other fixed assessment time points, total steps per day and cadence outcomes were descriptively summarized without formal group-by-time comparisons.

Sit-to-stand

Total sit-to-stand repetitions completed in 30 seconds were reported by participants who followed a validated self-guided video. 27 Sit-to-stand performance is a reliable and valid measure of physical function in older adult cancer survivors. 28 To contextualize potential functional limitations, age-specific and sex-specific cut points for high fall risk from the Centers for Disease Control and Prevention’s National Center for Injury Prevention and Control (Stopping Elderly Accidents, Deaths, and Injuries program) were used to identify participants who fell below these thresholds at baseline. 29 Participants were allowed to pause or discontinue the test if they experienced discomfort or safety concerns. Although 7 serious adverse events were reported throughout the year (4 new cancer diagnoses and 3 other serious illnesses or injuries), none were classified by the data safety monitoring committee as related to the study.

Mobility

Participants’ perceived mobility was assessed using a single item from the Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health measure, which asks about the extent to which individuals are able to carry out everyday physical activities. 30 Responses were recorded on a Likert scale ranging from “completely” to “not at all.” The PROMIS tool is a validated and reliable measure of self-reported physical function and has been shown to correlate with clinically meaningful levels of physical ability. 31 , 32 Because of small sample sizes in the response categories “mostly,” “moderately,” “a little,” and “not at all,” these were collapsed into a single group and compared with “completely” as a binary outcome.

Walk pace

Participants self-reported their usual walking pace using the following categories: easy (<2.0 mph), normal or average (2.0-2.9 mph), brisk (3.0-3.9 mph), very brisk (≥4.0 mph), or unable to walk. Because of the small sample sizes in the brisk and very brisk categories, these were combined into a single group and compared with easy and normal walking pace categories. Self-reported functional limitations, such as slower walking pace, have been associated with objectively measured gait speeds that are 0.08 m/s slower ( P < .001) among cancer survivors. 33 Slower gait speed, in turn, has been associated with lower overall survival. 33

Pain

Participants’ average pain was assessed using a single item from the PROMIS Mental Health measure, which asks participants to report a score ranging from 0 (no pain) to 10 (worst imaginable pain) to assess their average pain level. 34 Pain responses were collapsed into the following categories: 0, 1-2, or 3 or more. There is strong evidence that the PROMIS tool is a valid and reliable measure of pain in cancer survivors. 35-37

Statistical analysis

Participant baseline characteristics were summarized by study arm. To be included in these analyses, participants were required to have complete outcome data at baseline and at least 1 follow-up time point, resulting in 391 participants in these analyses. The full distribution for all self-reported measures are presented in Table S1 , but the collapsed categories used for analysis are reported in the main text. The study was adequately powered to detect meaningful differences in the primary and secondary endpoints of the study, which did not include Fitbit outcomes. 16 Total steps per day, daily peak 1-minute cadence and daily peak 30-minute cadence were calculated using rolling 14-day averages of daily means and standard deviations for descriptive analysis. Linear mixed models with generalized estimating equation modification were used to investigate within- and between-group changes in sit-to-stand performance across the 12-month intervention. Multinomial logistic regression models (1 per each time point) were used to investigate within- and between-group changes at each time point (ie, baseline, 3 months, 6 months, and 12 months) for self-reported walking pace and pain. Mobility was modeled as a binary outcome, estimating the predicted probability of being in the completely mobile category using logistic regression.

The primary analysis used an intention-to-treat (ITT) approach, with participants analyzed in the groups to which they were originally randomly assigned. Models were adjusted for baseline age but not for sex, because of the limited number of male participants ( n = 23, 5.9%). Additionally, 2 ad hoc analyses were conducted: (1) a per-protocol analysis by website compliance (control participants, adherent intervention, nonadherent intervention) and (2) an exploratory analysis restricted to participants with poor baseline physical health, defined as falling below sit-to-stand performance cut points, 29 limited mobility, an easy or normal walking pace, and any amount of pain so that we could restrict to those with an opportunity for change. Pairwise group comparisons were performed using estimated marginal means with Bonferroni adjustment applied to control for multiple testing.

Results

Among the 391 participants included in the analysis ( n = 121 control participants and n = 270 intervention group, Figure S1 ), the majority were female (94%, n = 368) with a mean (SD) age of 62 (8.5) years. Most participants identified as White (93%, n = 362), and 72% ( n = 282) were breast cancer survivors. As shown in Table 1 , baseline characteristics were comparable between the intervention and control groups. At baseline, sit-to-stand repetitions (median [quintile (Q1, Q3): intervention = 13 [11,15], control = 12 [11,14]), self-reported mobility (completely mobile: intervention = 77%, control = 82%), self-reported walk pace (normal: intervention = 60%, control = 52%), and self-reported pain (score of 1-2 [low average pain]: intervention = 47%, control = 51%) were also comparable between groups.

Fitbit results: total daily steps and peak cadence

Throughout the intervention, both groups of participants combined averaged 7397 (SD = 517) steps per day. The control participants averaged 7401 (SD = 541) steps, whereas intervention participants who were website-adherent averaged 7538 (SD = 502) steps and website nonadherent 7253 (SD = 466) steps ( Figure 1 ). Website-adherent participants appeared to have higher step counts than control or website nonadherent participants early in the study, but no prestudy data are available, so these descriptive data should be interpreted with caution.

Control and website nonadherent intervention participants had similar average peak 1-minute and peak 30-minute cadence (peak 1 minute = 136 per minute on average; 30-minute = 122 steps per minute on average), whereas website-adherent participants showed slightly higher cadence (peak 1 minute = 140 per minute on average; 30 minute = 125 per minute on average). Cadence patterns remained stable across groups throughout the study ( Figure 1 ).

ITT analysis

Sit-to-stand performance statistically significantly improved over time for both groups (all P < .001; Figure 2 , A ), suggesting the improvement was not a result of the intervention (group x time interaction P > .61). There were no statistically significant changes to self-reported mobility, walk pace, or pain in ITT models including all participants ( Figure 2 , B-D ).

Per-protocol analysis: stratified by website engagement

After stratifying by website adherence ( Figure 3 ), there was a statistically significant main time effect ( P < .001) where sit-to-stand repetitions increased from 13 to 15 in control participants and in nonadherent participants and 13 to 16 in adherent participants. Between-group comparisons and interactions in sit-to-stand performance were not statistically significant. Self-reported mobility probabilities did not differ by group at any time. At 6 months, adherent participants were more likely than controls to report a brisk or very brisk walk pace (difference = 18.6 percentage points; P = .04); no other walk pace or pain comparisons reached significance (all P > .95). Similarly, no significant group differences in average mobility or pain were detected at any time point (all P > .95).

Analysis among those with low sit-to-stand performance, poor mobility, easy or normal walk pace, or any pain at baseline

Participants with sit-to-stand performance above Centers for Disease Control and Prevention cut points 29 ( n = 300), complete mobility ( n = 306), brisk or very brisk walk pace ( n = 110), or zero average pain ( n = 95) at baseline were excluded for this stratified analysis ( Figure 4 ). Those with poor baseline sit-to-stand showed statistically significant improvements at all time points ( P < .0001), with no differences between groups. Similarly, participants with poor mobility at baseline demonstrated increased probabilities of complete mobility from 35%-45% at 3 months to 50%-56% at 12 months, without significant group differences. No significant differences were observed among participants with easy or normal walk pace or any baseline pain.

Discussion

Twelve months of access to a website designed to increase moderate-to-vigorous physical activity in cancer survivors did not improve overall physical health. However, benefits were observed for specific outcomes, step cadence, and sit-to-stand performance among participants who actively engaged with the website. No differences were found between groups in ITT analyses, but adherent participants showed higher peak cadence and greater gains in sit-to-stand ability, indicating some functional improvement. The intervention may have improved step cadence and sit-to-stand performance in adherent participants by enhancing lower-body strength, neuromuscular control, and movement confidence. 38 , 39 These findings suggest the intervention may be effective for engaged users, highlighting the need to better understand and promote adherence. Efforts to improve recruitment, particularly among men, are also needed to enhance impact.

The intervention had limited impact on physical health, likely due to a ceiling effect. Participants were relatively active at baseline (mean moderate-to-vigorous physical activity = 39 minutes per day; most >7000 steps per day), younger (mean age = 62 years), and primarily early stage survivors. Designed for less active individuals, the website may have been less relevant for this cohort. Modest benefits among adherent participants suggest potential utility for inactive survivors, though improved engagement strategies are needed. Limited variability in mobility and a 1-year follow-up may have constrained detection of change. Although the study was social cognitive theory informed, it did not assess key theoretical constructs, which future research should explore to better understand moderate-to-vigorous physical activity engagement.

Although self-reported physical health did not improve, device-based measures such as walking cadence and sit-to-stand performance improved among participants who adhered to the website, echoing findings from a 3-month home-based trial in lung cancer survivors who reported objective functional gains without corresponding changes in self-reported outcomes. 40 Website-based interventions have shown mixed results. For example, a wearables trial in inactive women with breast cancer increased activity and reduced sedentary time but did not improve self-reported quality of life. 41 Similarly, a 16-week feasibility trial by Harding et al. 42 in older, less healthy cancer survivors (mean age = 69 years; 50% with ≥3 comorbidities) showed only a modest increase in step cadence, from 100 to 108 steps per minutes, lower than the average 136-140 steps per minute observed in our study. Few web-based interventions have improved physical health in cancer survivors. Tailored programs, better adherence, and targeting less active individuals may enhance impact, while active survivors may require more advanced exercise plans.

This study has several strengths, including a large sample size achieved rapidly by leveraging a novel trial within a cohort design (enabling recruitment of >400 cancer survivors in just a few weeks) compared with months-years in traditional randomized controlled trials. We tested a carefully developed, feasible, and scalable website intervention over 12 months, with continuous Fitbit data, minimal attrition (91% completed 12-month assessment), and assessments at multiple time points. This approach directly responds to a top research priority identified by the American College of Sports Medicine Roundtable 6 : identifying accessible and sustainable strategies to increase physical activity among cancer survivors. Finally, as all participants were recruited from the CPS-3 prospective cohort, there will be long-term follow-up of all trial participants.

Despite its strengths, this study has several limitations to consider. First, website adherence among intervention participants varied. However, we were able to track usage through website clicks and analyze outcomes by compliance. Second, although the sample size was larger than most exercise oncology trials, it was still insufficient to examine outcomes by cancer type, stage, or treatment. Third, despite efforts to enroll participants not meeting moderate-to-vigorous physical activity guidelines, many had high baseline activity levels, which may limit generalizability to less active cancer survivors. Additionally, 93% of participants identified as White, and 94% identified as female, limiting the applicability of findings to more diverse populations and the generalizability of the study. Future studies should aim to recruit a more balanced sample to allow examination of sex and racial and ethnic differences. Finally, all assessments were conducted remotely. In clinical trials, physical function is often evaluated using in-person measures, which provide data other than self-report but are not feasible in the context of a trial embedded within a cohort study. Relying on remote assessments may introduce bias, such as reduced participant effort or overreporting of positive behaviors due to social desirability. To mitigate this, we used walking cadence as a remote, device-based measure of physical function. Cadence has been shown to reflect aging-related decline and predict earlier mortality. 43 , 44 However, few intervention studies have used cadence as a primary outcome, limiting our ability to compare findings across the literature.

Among cancer survivors, participation in this 12-month trial within a cohort was not associated with statistically significant changes in physical health outcomes overall, but among website adherent participants, improvements were seen in peak walking cadence and 30-second sit-to-stand performance. Although the activity tracker alone may offer meaningful support, 41 digital moderate-to-vigorous physical activity interventions, with low resource demands and broad reach, may provide a practical and scalable way to promote moderate-to-vigorous physical activity engagement among cancer survivors. Further research is needed to identify behavioral drivers of program engagement to inform effective strategies for enhancing adherence and maximizing the impact of digital moderate-to-vigorous physical activity interventions on physical health outcomes among cancer survivors.

Supplementary Material

Acknowledgments

The authors express sincere appreciation to all CPS-3 participants and to each member of the study and biospecimen management group. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention’s National Program of Cancer Registries and cancer registries supported by the National Cancer Institute’s Surveillance Epidemiology and End Results Program. Analyses were completed using the Science Cloud analytic platform developed by Manifold, Inc. The funder had no role in the design of the study; the collection, analysis, or interpretation of the data; or the writing of the manuscript and decision to submit it for publication.

Contributor Information

Lauren C Bates-Fraser, Department of Population Science, American Cancer Society, Atlanta, GA, United States.

Matt Masters, Department of Population Science, American Cancer Society, Atlanta, GA, United States.

Sidney M Donzella, Department of Epidemiology, University of Washington, Seattle, WA, United States.

Kathryn E Chiang, Department of Population Science, American Cancer Society, Atlanta, GA, United States; Winship Cancer Institute, Emory University, Atlanta, GA, United States.

Scott Whalen, Department of Population Science, American Cancer Society, Atlanta, GA, United States.

Charlie Zhong, Department of Population Science, American Cancer Society, Atlanta, GA, United States.

Christopher T V Swain, Department of Physiotherapy, University of Melbourne, Melbourne, Australia.

Rowena Tam, Department of Physical Therapy, San Diego State University, San Diego, CA, United States.

Kristen Sullivan, Patient Support, American Cancer Society, Atlanta, GA, United States.

Sheri J Hartman, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States.

Alpa V Patel, Department of Population Science, American Cancer Society, Atlanta, GA, United States.

Erika Rees-Punia, Department of Population Science, American Cancer Society, Atlanta, GA, United States.

Author contributions

Lauren C. Bates-Fraser (Formal analysis, Investigation, Writing—original draft, Writing—review & editing), Matt Masters (Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing—review & editing), Sidney M. Donzella (Formal analysis, Investigation, Writing—review & editing), Kathryn E. Chiang (Formal analysis, Investigation, Writing—original draft, Writing—review & editing), Scott Whalen (Data curation, Investigation, Methodology, Project administration, Writing—review & editing), Charlie Zhong (Conceptualization, Investigation, Writing—review & editing), Christopher T. V. Swain (Conceptualization, Investigation, Methodology, Writing—review & editing), Rowena Tam (Investigation, Writing—review & editing), Kristen Sullivan (Investigation, Writing—review & editing), Sheri J. Hartman (Investigation, Writing—review & editing), Alpa V. Patel (Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—review & editing), and Erika Rees-Punia (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing—original draft, Writing—review & editing)

Supplementary material

Supplementary material is available at JNCI Cancer Spectrum online.

Funding

The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-3 cohort. The study protocol was approved by the institutional review boards of Emory University and those of participating registries as required. The authors assume full responsibility for all analyses and interpretation of results. The views expressed here are those of the authors and do not necessarily represent the American Cancer Society or the American Cancer Society Cancer Action Network.

Conflicts of interest

The authors have no competing interests to declare that are relevant to the content of this article

Data availability

Data are available from the American Cancer Society by following the ACS Data Access Procedures ( https://www.cancer.org/research/population-science/research-collaboration.html ) for researchers who meet the criteria for access to confidential data. Please email cohort.data@cancer.org to inquire about access. Program code may be available on request.

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Associated Data

Supplementary Materials

Data Availability Statement

Data are available from the American Cancer Society by following the ACS Data Access Procedures ( https://www.cancer.org/research/population-science/research-collaboration.html ) for researchers who meet the criteria for access to confidential data. Please email cohort.data@cancer.org to inquire about access. Program code may be available on request.