scholarly journals An Activity Tracker–Guided Physical Activity Program for Patients Undergoing Radiotherapy: Protocol for a Prospective Phase III Trial (OnkoFit I and II Trials)

10.2196/28524 ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. e28524
Author(s):  
Franziska Hauth ◽  
Barbara Gehler ◽  
Andreas Michael Nieß ◽  
Katharina Fischer ◽  
Andreas Toepell ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Functional Assessment ◽  
Exercise Program ◽  
Daily Step ◽  
Patients With Cancer ◽  
Activity Tracker ◽  
Cancer Related Fatigue ◽  
Daily Step Count

Background The positive impact that physical activity has on patients with cancer has been shown in several studies over recent years. However, supervised physical activity programs have several limitations, including costs and availability. Therefore, our study proposes a novel approach for the implementation of a patient-executed, activity tracker–guided exercise program to bridge this gap. Objective Our trial aims to investigate the impact that an activity tracker–guided, patient-executed exercise program for patients undergoing radiotherapy has on cancer-related fatigue, health-related quality of life, and preoperative health status. Methods Patients receiving postoperative radiotherapy for breast cancer (OnkoFit I trial) or neoadjuvant, definitive, or postoperative treatment for other types of solid tumors (OnkoFit II trial) will be randomized (1:1:1) into 3-arm studies. Target accrual is 201 patients in each trial (50 patients per year). After providing informed consent, patients will be randomized into a standard care arm (arm A) or 1 of 2 interventional arms (arms B and C). Patients in arms B and C will wear an activity tracker and record their daily step count in a diary. Patients in arm C will receive personalized weekly targets for their physical activity. No further instructions will be given to patients in arm B. The target daily step goals for patients in arm C will be adjusted weekly and will be increased by 10% of the average daily step count of the past week until they reach a maximum of 6000 steps per day. Patients in arm A will not be provided with an activity tracker. The primary end point of the OnkoFit I trial is cancer-related fatigue at 3 months after the completion of radiotherapy. This will be measured by the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire. For the OnkoFit II trial, the primary end point is the overall quality of life, which will be assessed with the Functional Assessment of Cancer Therapy-General sum score at 6 months after treatment to allow for recovery after possible surgery. In parallel, blood samples from before, during, and after treatment will be collected in order to assess inflammatory markers. Results Recruitment for both trials started on August 1, 2020, and to date, 49 and 12 patients have been included in the OnkoFit I and OnkoFit II trials, respectively. Both trials were approved by the institutional review board prior to their initiation. Conclusions The OnkoFit trials test an innovative, personalized approach for the implementation of an activity tracker–guided training program for patients with cancer during radiotherapy. The program requires only a limited amount of resources. Trial Registration ClinicalTrials.gov NCT04506476; https://clinicaltrials.gov/ct2/show/NCT04506476. ClinicalTrials.gov NCT04517019; https://clinicaltrials.gov/ct2/show/NCT04517019. International Registered Report Identifier (IRRID) DERR1-10.2196/28524

scholarly journals An Activity Tracker–Guided Physical Activity Program for Patients Undergoing Radiotherapy: Protocol for a Prospective Phase III Trial (OnkoFit I and II Trials) (Preprint)

2021 ◽  
Author(s):  
Franziska Hauth ◽  
Barbara Gehler ◽  
Andreas Michael Nieß ◽  
Katharina Fischer ◽  
Andreas Toepell ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Functional Assessment ◽  
Exercise Program ◽  
Daily Step ◽  
Patients With Cancer ◽  
Activity Tracker ◽  
Cancer Related Fatigue ◽  
Daily Step Count

BACKGROUND The positive impact that physical activity has on patients with cancer has been shown in several studies over recent years. However, supervised physical activity programs have several limitations, including costs and availability. Therefore, our study proposes a novel approach for the implementation of a patient-executed, activity tracker–guided exercise program to bridge this gap. OBJECTIVE Our trial aims to investigate the impact that an activity tracker–guided, patient-executed exercise program for patients undergoing radiotherapy has on cancer-related fatigue, health-related quality of life, and preoperative health status. METHODS Patients receiving postoperative radiotherapy for breast cancer (OnkoFit I trial) or neoadjuvant, definitive, or postoperative treatment for other types of solid tumors (OnkoFit II trial) will be randomized (1:1:1) into 3-arm studies. Target accrual is 201 patients in each trial (50 patients per year). After providing informed consent, patients will be randomized into a standard care arm (arm A) or 1 of 2 interventional arms (arms B and C). Patients in arms B and C will wear an activity tracker and record their daily step count in a diary. Patients in arm C will receive personalized weekly targets for their physical activity. No further instructions will be given to patients in arm B. The target daily step goals for patients in arm C will be adjusted weekly and will be increased by 10% of the average daily step count of the past week until they reach a maximum of 6000 steps per day. Patients in arm A will not be provided with an activity tracker. The primary end point of the OnkoFit I trial is cancer-related fatigue at 3 months after the completion of radiotherapy. This will be measured by the Functional Assessment of Chronic Illness Therapy-Fatigue questionnaire. For the OnkoFit II trial, the primary end point is the overall quality of life, which will be assessed with the Functional Assessment of Cancer Therapy-General sum score at 6 months after treatment to allow for recovery after possible surgery. In parallel, blood samples from before, during, and after treatment will be collected in order to assess inflammatory markers. RESULTS Recruitment for both trials started on August 1, 2020, and to date, 49 and 12 patients have been included in the OnkoFit I and OnkoFit II trials, respectively. Both trials were approved by the institutional review board prior to their initiation. CONCLUSIONS The OnkoFit trials test an innovative, personalized approach for the implementation of an activity tracker–guided training program for patients with cancer during radiotherapy. The program requires only a limited amount of resources. CLINICALTRIAL ClinicalTrials.gov NCT04506476; https://clinicaltrials.gov/ct2/show/NCT04506476. ClinicalTrials.gov NCT04517019; https://clinicaltrials.gov/ct2/show/NCT04517019. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/28524

scholarly journals Examining physical activity and quality of life in adults with autism spectrum disorder and intellectual disability

2021 ◽  
pp. 174462952110334
Author(s):  
Brianne Tomaszewski ◽  
Melissa N Savage ◽  
Kara Hume
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Intellectual Disability ◽  
Step Count ◽  
Daily Step ◽  
Adults With Autism ◽  
Activity Tracker ◽  
Daily Step Count ◽  
The Relationship

Adults with autism and co-occurring intellectual disability engage in low levels of physical activity and are at increased risk of developing secondary health conditions attributed to physical inactivity compared to adults in the general population. Few studies have examined the use of objective measures to characterize physical activity levels for adults with autism and intellectual disability. The current study aimed to examine the relationship between physical activity, using an activity tracker, and quality of life in adults with autism and intellectual disability. In the current study, 38 adults with autism and intellectual disability, ages 18–55, wore a Fitbit Flex 2® activity tracker for 1 week, and completed the Quality of Life Questionnaire. The relationship between average daily step count quality of life was examined. Most adults in the sample were overweight and taking fewer daily steps than recommended guidelines. Increased average daily step count was significantly associated with quality of life.

scholarly journals Assessing the Correlation Between Physical Activity and Quality of Life in Advanced Lung Cancer

2016 ◽  
Vol 17 (1) ◽  
pp. 73-79 ◽  
Author(s):  
Brett C. Bade ◽  
Mary C. Brooks ◽  
Sloan B. Nietert ◽  
Ansley Ulmer ◽  
D. David Thomas ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Lung Cancer ◽  
Correlation Coefficients ◽  
Step Count ◽  
Advanced Lung Cancer ◽  
Advanced Stage ◽  
Daily Step ◽  
Daily Step Count

Background and objective. Increasing physical activity (PA) is safe and beneficial in lung cancer (LC) patients. Advanced-stage LC patients are under-studied and have worse symptoms and quality of life (QoL). We evaluated the feasibility of monitoring step count in advanced LC as well as potential correlations between PA and QoL. Methods. This is a prospective, observational study of 39 consecutive patients with advanced-stage LC. Daily step count over 1 week (via Fitbit Zip), QoL, dyspnea, and depression scores were collected. Spearman rank testing was used to assess correlations. Correlation coefficients (ρ) >0.3 or <−0.3 (more and less correlated, respectively) were considered potentially clinically significant. Results. Most (83%) of the patients were interested in participating, and 67% of those enrolled were adherent with the device. Of those using the device (n = 30), the average daily step count was 4877 (range = 504-12 118) steps/d. Higher average daily step count correlated with higher QoL (ρ = 0.46), physical (ρ = 0.61), role (ρ = 0.48), and emotional functioning (ρ = 0.40) scores as well as lower depression (ρ = −0.40), dyspnea (ρ = −0.54), and pain (ρ = −0.37) scores. Conclusion. Remote PA monitoring (Fitbit Zip) is feasible in advanced-stage LC patients. Interest in participating in this PA study was high with comparable adherence to other PA studies. In those utilizing the device, higher step count correlates with higher QoL as well as lower dyspnea, pain, and depression scores. PA monitoring with wearable devices in advanced-stage LC deserves further study.

scholarly journals Effect of the LIVESTRONG at the YMCA exercise program on physical activity, fitness, quality of life, and fatigue in cancer survivors

Cancer ◽  
2016 ◽  
Vol 123 (7) ◽  
pp. 1249-1258 ◽  
Author(s):  
Melinda L. Irwin ◽  
Brenda Cartmel ◽  
Maura Harrigan ◽  
Fangyong Li ◽  
Tara Sanft ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Cancer Survivors ◽  
Exercise Program

scholarly journals Randomized Controlled Trial of an Educational Intervention for Managing Fatigue in Women Receiving Adjuvant Chemotherapy for Early-Stage Breast Cancer

2005 ◽  
Vol 23 (25) ◽  
pp. 6027-6036 ◽  
Author(s):  
Patsy Yates ◽  
Sanchia Aranda ◽  
Maryanne Hargraves ◽  
Bev Mirolo ◽  
Alexandra Clavarino ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Randomized Controlled Trial ◽  
Functional Assessment ◽  
Controlled Trial ◽  
Intervention Group ◽  
Randomized Controlled ◽  
Cancer Related Fatigue ◽  
Numeric Rating

PurposeTo evaluate the efficacy of a psychoeducational intervention in improving cancer-related fatigue.Patients and MethodsThis randomized controlled trial involved 109 women commencing adjuvant chemotherapy for stage I or II breast cancer in five chemotherapy treatment centers. Intervention group patients received an individualized fatigue education and support program delivered in the clinic and by phone over three 10- to 20-minute sessions 1 week apart. Instruments included a numeric rating scale assessing confidence with managing fatigue; 11-point numeric rating scales measuring fatigue at worst, average, and best; the Functional Assessment of Cancer Therapy–Fatigue and Piper Fatigue Scales; the Cancer Self-Efficacy Scale; the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30; and the Hospital Anxiety and Depression Scale. For each outcome, separate analyses of covariance of change scores between baseline (T1) and the three follow-up time points (T2, T3, and T4) were conducted, controlling for the variable's corresponding baseline value.ResultsCompared with the intervention group, mean difference scores between the baseline (T1) and immediate after the test (T2) assessments increased significantly more for the control group for worst and average fatigue, Functional Assessment of Cancer Therapy–Fatigue, and Piper fatigue severity and interference measures. These differences were not observed between baseline and T3 and T4 assessments. No significant differences were identified for any pre- or post-test change scores for confidence with managing fatigue, cancer self-efficacy, anxiety, depression, or quality of life.ConclusionPreparatory education and support has the potential to assist women to cope with cancer-related fatigue in the short term. However, further research is needed to identify ways to improve the potency and sustainability of psychoeducational interventions for managing cancer-related fatigue.

scholarly journals Insights and Perspectives in the Clinical and Operational Management of Cancer-Related Anemia

2010 ◽  
Vol 8 (Suppl_7) ◽  
pp. S-38-S-55 ◽  
Author(s):  
Jennifer M. Hinkel ◽  
Edward C. Li ◽  
Stephen L. Sherman
Keyword(s):  
Quality Of Life ◽  
Radiation Therapy ◽  
Surgical Interventions ◽  
Patients With Cancer ◽  
Cancer Related Fatigue ◽  
Operational Management ◽  
Life Issues ◽  
Economic Perspectives ◽  
Cancer Related Anemia

Management of anemia in patients with cancer presents challenges from clinical, operational, and economic perspectives. Clinically, anemia in these patients may result from treatment (chemotherapy, radiation therapy, or surgical interventions) or from the malignancy itself. Anemia not only contributes to cancer-related fatigue and other quality of life issues, but also affects prognosis. From the operational perspective, a patient with cancer who is also anemic may consume more laboratory, pharmacy, and clinical resources than other patients with cancer.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: a systematic review and meta-analysis

2019 ◽  
Vol 54 (20) ◽  
pp. 1188-1194 ◽  
Author(s):  
Juliana S Oliveira ◽  
Cathie Sherrington ◽  
Elizabeth R Y Zheng ◽  
Marcia Rodrigues Franco ◽  
Anne Tiedemann
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Physical Activity ◽  
Systematic Review ◽  
Older People ◽  
Activity Levels ◽  
Activity Tracker ◽  
Activity Trackers ◽  
The Impact

BackgroundOlder people are at high risk of physical inactivity. Activity trackers can facilitate physical activity. We aimed to investigate the effect of interventions using activity trackers on physical activity, mobility, quality of life and mental health among people aged 60+ years.MethodsFor this systematic review, we searched eight databases, including MEDLINE, Embase and CENTRAL from inception to April 2018. Randomised controlled trials of interventions that used activity trackers to promote physical activity among people aged 60+ years were included in the analyses. The study protocol was registered with PROSPERO, number CRD42017065250.ResultsWe identified 23 eligible trials. Interventions using activity trackers had a moderate effect on physical activity (23 studies; standardised mean difference (SMD)=0.55; 95% CI 0.40 to 0.70; I2=86%) and increased steps/day by 1558 (95% CI 1099 to 2018 steps/day; I2=92%) compared with usual care, no intervention and wait-list control. Longer duration activity tracker-based interventions were more effective than short duration interventions (18 studies, SMD=0.70; 95% CI 0.47 to 0.93 vs 5 studies, SMD=0.14; 95% CI −0.26 to 0.54, p for comparison=0.02). Interventions that used activity trackers improved mobility (three studies; SMD=0.61; 95% CI 0.31 to 0.90; I2=10%), but not quality of life (nine studies; SMD=0.09; 95% CI −0.07 to 0.25; I2=45%). Only one trial included mental health outcomes and it reported similar effects of the activity tracker intervention compared with control.ConclusionsInterventions using activity trackers improve physical activity levels and mobility among older people compared with control. However, the impact of activity tracker interventions on quality of life, and mental health is unknown.

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