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 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 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 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 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.

scholarly journals The Adverse Impact of Glaucoma on Psychological Function and Daily Physical Activity

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Wenbin Huang ◽  
Kai Gao ◽  
Yaoming Liu ◽  
Mengyin Liang ◽  
Xiulan Zhang
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Social Function ◽  
Step Count ◽  
Control Group ◽  
Advanced Stage ◽  
Daily Step ◽  
Daily Step Count ◽  
Glaucoma Group

Purpose. To evaluate the impact of glaucoma on vision-related quality of life and physical activity. Methods. This study included 50 glaucoma patients and 50 healthy control subjects. Sociodemographic and clinical data were collected from all subjects. A Chinese version of the NEI VFQ-25 was used to evaluate the quality of life. Objective physical activity was assessed by wearing an accelerometer for 7 consecutive days. Results. No significant difference was found in sociodemographic data between the two groups (all p<0.05). Visual acuity and visual field scores were worse in the glaucoma group than in the control group (all p<0.001). The VFQ-25 scores indicated significantly lower scores for ocular pain, social function, mental health, role difficulties, and color vision in the glaucoma group than in the normal group (all p<0.05). The average daily step count was lower in the glaucoma group than in the normal group. High, moderate, and low average daily step counts in the glaucoma group were associated with early-, moderate-, and advanced-stage glaucoma, respectively, while the step count was significantly lower in the advanced-stage glaucoma group than in the control group p=0.037. A positive relationship was found between the average daily step count and social function and mental health (both p<0.05). Conclusions. We demonstrated an adverse impact of glaucoma on psychological function and daily physical activity. Social function and mental health showed declines in glaucoma patients, and physical activity was limited in patients with advanced-stage glaucoma.

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 (Preprint)

2020 ◽  
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.

scholarly journals Acceptability, Feasibility and Preliminary Evaluation of a Novel, Personalised, Home-Based Physical Activity Intervention for Chronic Heart Failure (Active-at-Home-HF): a Pilot Study

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Nduka C. Okwose ◽  
Leah Avery ◽  
Nicola O’Brien ◽  
Sophie Cassidy ◽  
Sarah J. Charman ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Heart Failure ◽  
Pilot Study ◽  
Chronic Heart Failure ◽  
Physical Activity Intervention ◽  
Step Count ◽  
Home Based ◽  
At Home

Abstract Purpose Less than 10% of heart failure patients in the UK participate in cardiac rehabilitation programmes. The present pilot study evaluated feasibility, acceptability and physiological effects of a novel, personalised, home-based physical activity intervention in chronic heart failure. Methods Twenty patients (68 ± 7 years old, 20% females) with stable chronic heart failure due to reduced left ventricular ejection fraction (31 ± 8 %) participated in a single-group, pilot study assessing the feasibility and acceptability of a 12-week personalised home-based physical activity intervention aiming to increase daily number of steps by 2000 from baseline (Active-at-Home-HF). Patients completed cardiopulmonary exercise testing with non-invasive gas exchange and haemodynamic measurements and quality of life questionnaire pre- and post-intervention. Patients were supported weekly via telephone and average weekly step count data collected using pedometers. Results Forty-three patients were screened and 20 recruited into the study. Seventeen patients (85%) completed the intervention, and 15 (75%) achieved the target step count. Average step count per day increased significantly from baseline to 3 weeks by 2546 (5108 ± 3064 to 7654 ± 3849, P = 0.03, n = 17) and was maintained until week 12 (9022 ± 3942). Following completion of the intervention, no adverse events were recorded and quality of life improved by 4 points (26 ± 18 vs. 22 ± 19). Peak exercise stroke volume increased by 19% (127 ± 34 vs. 151 ± 34 m/beat, P = 0.05), while cardiac index increased by 12% (6.8 ± 1.5 vs. 7.6 ± 2.0 L/min/m2, P = 0.19). Workload and oxygen consumption at anaerobic threshold also increased by 16% (49 ± 16 vs. 59 ± 14 watts, P = 0.01) and 10% (11.5 ± 2.9 vs. 12.8 ± 2.2 ml/kg/min, P = 0.39). Conclusion The Active-at-Home-HF intervention is feasible, acceptable and effective for increasing physical activity in CHF. It may lead to improvements in quality of life, exercise tolerance and haemodynamic function. Trial Registration www.clinicaltrials.gov NCT0367727. Retrospectively registered on 17 September 2018.

The translation of preschoolers’ physical activity guidelines into a daily step count target

2014 ◽  
Vol 33 (10) ◽  
pp. 1051-1057 ◽  
Author(s):  
Marieke De Craemer ◽  
Ellen De Decker ◽  
Ilse De Bourdeaudhuij ◽  
Maïté Verloigne ◽  
Yannis Manios ◽  
...  
Keyword(s):  
Physical Activity ◽  
Step Count ◽  
Daily Step ◽  
Physical Activity Guidelines ◽  
Daily Step Count

Abstract 11: mActive: A Blinded, Randomized mHealth Trial Supporting Digital Tracking and Smart Texting for Promotion of Physical Activity

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Seth S Martin ◽  
David I Feldman ◽  
Roger S Blumenthal ◽  
Steven R Jones ◽  
Wendy S Post ◽  
...  
Keyword(s):  
Physical Activity ◽  
Phase I ◽  
Real Time ◽  
Phase Ii ◽  
The United States ◽  
Step Count ◽  
Daily Step ◽  
Baseline Activity ◽  
Daily Step Count ◽  
Digital Tracking

Introduction: The recent advent of smartphone-linked wearable pedometers offers a novel opportunity to promote physical activity using mobile health (mHealth) technology. Hypothesis: We hypothesized that digital activity tracking and smart (automated, real-time, personalized) texting would increase physical activity. Methods: mActive (NCT01917812) was a 5-week, blinded, sequentially-randomized, parallel group trial that enrolled patients at an academic preventive cardiovascular center in Baltimore, MD, USA from January 17 th to May 20 th , 2014. Eligible patients were 18-69 year old smartphone users who reported low leisure-time physical activity by a standardized survey. After establishing baseline activity during a 1-week blinded run-in, we randomized 2:1 to unblinded or blinded tracking in phase I (2 weeks), then randomized unblinded participants 1:1 to receive or not receive smart texts in phase II (2 weeks). Smart texts provided automated, personalized, real-time coaching 3 times/day towards a daily goal of 10,000 steps. The primary outcome was change in daily step count. Results: Forty-eight patients (22 women, 26 men) enrolled with a mean (SD) age of 58 (8) years, body mass index of 31 (6), and baseline daily step count of 9670 (4350). The phase I change in activity was non-significantly higher in unblinded participants versus blinded controls by 1024 steps/day (95% CI -580-2628, p=0.21). In phase II, smart text receiving participants increased their daily steps over those not receiving texts by 2534 (1318-3750, p<0.001) and over blinded controls by 3376 (1951-4801, p<0.001). The unblinded-texts group had the highest proportion attaining the 10,000 steps/day goal (p=0.02) (Figure). Conclusions: In present-day adult smartphone users receiving preventive cardiovascular care in the United States, a technologically-integrated mHealth strategy combining digital tracking with automated, personalized, real-time text message coaching resulted in a large short-term increase in physical activity.

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