Consumer-wearable activity trackers have been used for monitoring health-related metrics to estimate steps, distance, physical activity, energy expenditure, and sleep. The purpose of this mini review was to summarize ...Consumer-wearable activity trackers have been used for monitoring health-related metrics to estimate steps, distance, physical activity, energy expenditure, and sleep. The purpose of this mini review was to summarize the evidence for validity of the most popular wrist-worn activity tracker (Fitbit) to estimate those health-related metrics in Parkinson disease. We researched full-length English studies in PubMed, Science Direct, Google Scholar, and Scopus, through September, 2021. In total, 27 studies and a textbook description were included in the review. To adapt consumer-wearable activity trackers for evaluating health-related metrics in Parkinson’s disease (PD) patients, there may be some points to be elucidated and conquered. First, measurement accuracy and precision are required. Second, inter-device reliability for measuring steps, distance, and energy expenditure must be considered. Third, wearability: there are some types of device such as wrist-worn, ankle-worn, belt-fixed, and so on. Overall, Fitbit has advantage for these points. This mini review indicates that Fitbit has enough measurement accuracy and precision to estimate health-related metrics of PD patients including amount of step, physical activity energy expenditure, and quality of sleep.展开更多
Research Background: Compared to the general population, people experiencing age-related cognitive decline are more likely to have low levels of physical activity and sleep problems. Sufficient physical activity and q...Research Background: Compared to the general population, people experiencing age-related cognitive decline are more likely to have low levels of physical activity and sleep problems. Sufficient physical activity and quality sleep are protective factors against cognitive decline and poor health and can improve coping with stressors. The “Active Feedback” intervention comprises a wearable activity and sleep tracker (Fitbit), access to Fitbit software healthy lifestyle software apps;one session with Memory Assessment Service (MAS) staff providing physical activity and sleep hygiene advice and two further engagement, discussion, and feedback sessions. Purpose/Aim: This study investigates the acceptability and feasibility of Active Feedback and the effect on stress, mental wellbeing, and sleep quality, and the links between these factors. Methods: An open-label patient cohort design with no control group was used. Pre-intervention, 4-week and 8-week intervention assessments were performed using participant self-report measures: Perceived Stress Scale (PSS), Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), and Sleep Conditioning Index (SCI). Twenty-five participants completed an eight-week three-session intervention (18 males and 7 females), with the age range of 66 - 84 years old, and average age of 73.8 years (SD = 5.09). Fifteen participants had a diagnosis of MCI, ten participants did not. Results: There were non-significant improvements in SCI scores from 21.0 (SD = 8.84) to 21.6 (SD = 6.20) at 8 weeks, PSS scores from 17.5 (SD = 5.89) to 17.0 (SD = 6.20) at 8 weeks, and WEMWBS scores from 46.9 (SD = 9.23) to 48.8 (SD = 9.69) at 8 weeks. There were negative correlations between WEMWBS and PSS. Conclusion: Active Feedback intervention was found to be feasible and acceptable. Active Feedback could be enhanced to include motivational interviewing and goal setting.展开更多
Background: Physical activity and sleep are interconnected with mental health, physical health, wellbeing, quality of life, cognition, and functioning. Compared to the general population, people who experience psychos...Background: Physical activity and sleep are interconnected with mental health, physical health, wellbeing, quality of life, cognition, and functioning. Compared to the general population, people who experience psychosis are more likely to have low levels of physical activity, high levels of sedentary behaviour, and sleep problems. Intervention: The Well-Track intervention addresses these issues through: provision of a wearable activity and sleep tracker (Fitbit);physical activity and sleep hygiene advice;a brief motivational interview;a goal-setting workbook;and three engagement, feedback and discussion sessions with early intervention in psychosis (EIP) staff. Participants: Thirty participants using an EIP service took part in an eight-week intervention. Thirteen participants (6 males;7 females) with an age range of 18 to 61 years old (M = 28 years) took part in an interview. Methods: A qualitative approach was used to conduct in-depth semi-structured interviews. Thematic and content analyses were employed to analyse the data. Results: Participants set goals, made lifestyle changes to their daily routine and integrated a Fitbit and its functions into their lives that resulted in more physical activity and enabled more effective sleep. This resulted in improved self-management, positive feelings and thoughts, motivation, confidence, social engagement, mood, health, and wellbeing. Participants made progress towards goals they had set. Conclusion: Well-Track has been successfully integrated into an EIP service and it could be delivered through all EIP and other healthcare services where there is a need to promote healthy lifestyle behaviours.展开更多
Purpose:This systematic review and meta-analysis aimed to evaluate the effect of wearable devices for improving physical activity and healthrelated outcomes in cancer survivors.Methods:CINAHL,Cochrane,Ebscohost,MEDLIN...Purpose:This systematic review and meta-analysis aimed to evaluate the effect of wearable devices for improving physical activity and healthrelated outcomes in cancer survivors.Methods:CINAHL,Cochrane,Ebscohost,MEDLINE,Pubmed,ProQuest Health and Medical Complete,ProQuest Nursing and Allied Health Source,ScienceDirect,and SPORTDiscus databases were searched for randomized controlled trials published before September 1,2020,that evaluated interventions involving wearable devices in cancer survivors.Standardized mean differences(SMDs)were calculated to assess effects on physical activity and health-related outcomes.Subgroup analyses were conducted to assess whether the effects differed by interventions and cancer characteristics.Risk of bias was assessed using the Cochrane risk of bias tool.Results:Thirty-five trials were included(breast cancer,n=15,43%).Intervention durations ranged between 4 weeks and 1 year.Most trials(n=25,71%)involved pedometer-based physical activity interventions.Seven(20%)involved Fitbit-based interventions,and 3(9%)involved other wearable physical activity trackers(e.g.,Polar,Garmin).Compared to usual care,wearable devices had moderate-to-large effects(SMD range 0.54-0.87,p<0.001)on moderate-intensity physical activity,moderate-to-vigorous-intensity physical activity,total physical activity,and daily steps.Compared to usual care,those in the intervention had higher quality of life,aerobic fitness,physical function,and reduced fatigue(SMD range=0.18-0.66,all p<0.05).Conclusion:Wearable physical activity trackers and pedometers are effective tools that increase physical activity and improve health-related outcomes in individuals with cancer.Identifying how these devices can be implemented for longer-term use with other intervention components remains an area for future research.展开更多
Background: Compared to the general population, people who are at a high risk of or experience severe mental illness (SMI) such as psychosis, are more likely to have low levels of physical activity, high levels of sed...Background: Compared to the general population, people who are at a high risk of or experience severe mental illness (SMI) such as psychosis, are more likely to have low levels of physical activity, high levels of sedentary behaviour, and sleep problems. Intervention: The Well-Track intervention comprises a wearable activity and sleep tracker (Fitbit);one session with mental health service staff providing physical activity and sleep hygiene advice;a brief motivational interview;completing a goal setting workbook;and one or two further engagement, feedback and discussion sessions. Participants: Twenty-four participants using an early intervention in psychosis (EIP) or at-risk mental state (ARMS) service completed an eight-week, three session intervention (14 males and 10 females), with an age range of 18 - 61, and average age of 27.75 years. Methods: An open-label patient cohort design with no control group. Pre-intervention, 4-week and 8-week intervention assessments using participant self-report measures: Patient Health Questionnaire (PHQ-9) (depression), Warwick–Edinburgh Mental Wellbeing Scale (WEMWBS), and Sleep Conditioning Index (SCI);and clinician measurement of body weight. Results: Mean scores showed a significant improvement in PHQ-9 from 9.29 (SD 5.89) to 5.58 (SD 3.68) at 4 weeks and to 5.83 (SD 4.40) at 8 weeks, with large effect sizes. For those who met a diagnosis of depression at baseline, at 4 week follow-up seven participants (26%) experienced remission and nine (33%) reliable improvement, and at 8 week follow-up four (21%) experienced remission and seven (37%) reliable improvement. WEMWBS scores significantly improved, from 44.04 (SD 9.44) to 48.54 (SD 8.71) at 4 weeks and to 48.67 (SD 8.76) at 8 weeks, with large effect sizes. Body weight did not change significantly, remaining unchanged at 4 weeks and reduced from a mean of 82.8 kg (baseline) to 80.15 kg at 8 weeks, a reduction of 2.65 kg. Conclusion: Well-Track was integrated into an EIP and ARMS service and was found to be beneficial in terms of wellbeing, depression, sleep, and preventing weight gain (either as a two or three engagement point intervention). Well-Track could be delivered through EIP and ARMS services to promote healthy lifestyle behaviours.展开更多
Background: In psychosis physical activity, sleep, mental health, physical health, wellbeing, quality of life, cognition and functioning are interconnected. People who experience psychosis are more likely than the gen...Background: In psychosis physical activity, sleep, mental health, physical health, wellbeing, quality of life, cognition and functioning are interconnected. People who experience psychosis are more likely than the general population to have low levels of physical activity, high levels of sedentary behaviour and sleep problems. This project was innovative in seeking to address these issues through provision of a wearable activity and sleep tracker (a Fitbit) and sleep hygiene advice. Participants: Participants using an early intervention psychosis (EIP) service took part in an eight-week intervention, which incorporated the provision of a Fitbit, sleep hygiene advice as well as three engagement, feedback and discussion points with a clinician. Methods: A qualitative approach was used to conduct in-depth semi-structured interviews with 12 of the 25 intervention participants (5 male;7 female). Thematic and content analyses were employed to analyse the data. Results: Participants provided valuable insights into their experience of sleep, exercise, Fitbit use and sleep hygiene advice use. It was found that participants placed a high value on effective night time sleep, recognized improvements in physical activity and noted a positive effect on mood and wellbeing as a result of Fitbit use. The negative impact of having ineffective night time sleep and insufficient physical activity was described. Participants demonstrated a good level of understanding of the connection between sleep, exercise, wellbeing, and health. Conclusion: Participants reported the Fitbit and sleep hygiene advice received through an EIP service to be beneficial for improved levels of physical activity and exercise, and more effective sleep. This is a simple and low cost intervention which could be made widely available through EIP and other mental health services.展开更多
A star identification algorithm was developed for a charge-coupled device （CCD） or complementary metal-oxide-semiconductor （CMOS） autonomous star tracker to acquire 3-axis attitude information for a lost-in-space ...A star identification algorithm was developed for a charge-coupled device （CCD） or complementary metal-oxide-semiconductor （CMOS） autonomous star tracker to acquire 3-axis attitude information for a lost-in-space spacecraft. The algorithm took advantage of an efficient on-board database and an original “4- star matching” pattern recognition strategy to achieve fast and reliable star identification. The on-board database was composed of a brightness independent guide star catalog （mission catalog） and a K-vector star pair catalog. The star pattern recognition method involved direct location of star pair candidates and a sim- ple array matching procedure. Tests of the algorithm with a CMOS active pixel sensor （APS） star tracker result in a 99.9% success rate for star identification for lost-in-space 3-axis attitude acquisition when the angular measurement accuracy of the star tracker is at least 0.01°. The brightness independent algorithm requires relatively higher measurement accuracy of the star apparent positions that can be easily achieved by CCD or CMOS sensors along with subpixel centroiding techniques.展开更多
文摘Consumer-wearable activity trackers have been used for monitoring health-related metrics to estimate steps, distance, physical activity, energy expenditure, and sleep. The purpose of this mini review was to summarize the evidence for validity of the most popular wrist-worn activity tracker (Fitbit) to estimate those health-related metrics in Parkinson disease. We researched full-length English studies in PubMed, Science Direct, Google Scholar, and Scopus, through September, 2021. In total, 27 studies and a textbook description were included in the review. To adapt consumer-wearable activity trackers for evaluating health-related metrics in Parkinson’s disease (PD) patients, there may be some points to be elucidated and conquered. First, measurement accuracy and precision are required. Second, inter-device reliability for measuring steps, distance, and energy expenditure must be considered. Third, wearability: there are some types of device such as wrist-worn, ankle-worn, belt-fixed, and so on. Overall, Fitbit has advantage for these points. This mini review indicates that Fitbit has enough measurement accuracy and precision to estimate health-related metrics of PD patients including amount of step, physical activity energy expenditure, and quality of sleep.
文摘Research Background: Compared to the general population, people experiencing age-related cognitive decline are more likely to have low levels of physical activity and sleep problems. Sufficient physical activity and quality sleep are protective factors against cognitive decline and poor health and can improve coping with stressors. The “Active Feedback” intervention comprises a wearable activity and sleep tracker (Fitbit), access to Fitbit software healthy lifestyle software apps;one session with Memory Assessment Service (MAS) staff providing physical activity and sleep hygiene advice and two further engagement, discussion, and feedback sessions. Purpose/Aim: This study investigates the acceptability and feasibility of Active Feedback and the effect on stress, mental wellbeing, and sleep quality, and the links between these factors. Methods: An open-label patient cohort design with no control group was used. Pre-intervention, 4-week and 8-week intervention assessments were performed using participant self-report measures: Perceived Stress Scale (PSS), Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), and Sleep Conditioning Index (SCI). Twenty-five participants completed an eight-week three-session intervention (18 males and 7 females), with the age range of 66 - 84 years old, and average age of 73.8 years (SD = 5.09). Fifteen participants had a diagnosis of MCI, ten participants did not. Results: There were non-significant improvements in SCI scores from 21.0 (SD = 8.84) to 21.6 (SD = 6.20) at 8 weeks, PSS scores from 17.5 (SD = 5.89) to 17.0 (SD = 6.20) at 8 weeks, and WEMWBS scores from 46.9 (SD = 9.23) to 48.8 (SD = 9.69) at 8 weeks. There were negative correlations between WEMWBS and PSS. Conclusion: Active Feedback intervention was found to be feasible and acceptable. Active Feedback could be enhanced to include motivational interviewing and goal setting.
文摘Background: Physical activity and sleep are interconnected with mental health, physical health, wellbeing, quality of life, cognition, and functioning. Compared to the general population, people who experience psychosis are more likely to have low levels of physical activity, high levels of sedentary behaviour, and sleep problems. Intervention: The Well-Track intervention addresses these issues through: provision of a wearable activity and sleep tracker (Fitbit);physical activity and sleep hygiene advice;a brief motivational interview;a goal-setting workbook;and three engagement, feedback and discussion sessions with early intervention in psychosis (EIP) staff. Participants: Thirty participants using an EIP service took part in an eight-week intervention. Thirteen participants (6 males;7 females) with an age range of 18 to 61 years old (M = 28 years) took part in an interview. Methods: A qualitative approach was used to conduct in-depth semi-structured interviews. Thematic and content analyses were employed to analyse the data. Results: Participants set goals, made lifestyle changes to their daily routine and integrated a Fitbit and its functions into their lives that resulted in more physical activity and enabled more effective sleep. This resulted in improved self-management, positive feelings and thoughts, motivation, confidence, social engagement, mood, health, and wellbeing. Participants made progress towards goals they had set. Conclusion: Well-Track has been successfully integrated into an EIP service and it could be delivered through all EIP and other healthcare services where there is a need to promote healthy lifestyle behaviours.
文摘Purpose:This systematic review and meta-analysis aimed to evaluate the effect of wearable devices for improving physical activity and healthrelated outcomes in cancer survivors.Methods:CINAHL,Cochrane,Ebscohost,MEDLINE,Pubmed,ProQuest Health and Medical Complete,ProQuest Nursing and Allied Health Source,ScienceDirect,and SPORTDiscus databases were searched for randomized controlled trials published before September 1,2020,that evaluated interventions involving wearable devices in cancer survivors.Standardized mean differences(SMDs)were calculated to assess effects on physical activity and health-related outcomes.Subgroup analyses were conducted to assess whether the effects differed by interventions and cancer characteristics.Risk of bias was assessed using the Cochrane risk of bias tool.Results:Thirty-five trials were included(breast cancer,n=15,43%).Intervention durations ranged between 4 weeks and 1 year.Most trials(n=25,71%)involved pedometer-based physical activity interventions.Seven(20%)involved Fitbit-based interventions,and 3(9%)involved other wearable physical activity trackers(e.g.,Polar,Garmin).Compared to usual care,wearable devices had moderate-to-large effects(SMD range 0.54-0.87,p<0.001)on moderate-intensity physical activity,moderate-to-vigorous-intensity physical activity,total physical activity,and daily steps.Compared to usual care,those in the intervention had higher quality of life,aerobic fitness,physical function,and reduced fatigue(SMD range=0.18-0.66,all p<0.05).Conclusion:Wearable physical activity trackers and pedometers are effective tools that increase physical activity and improve health-related outcomes in individuals with cancer.Identifying how these devices can be implemented for longer-term use with other intervention components remains an area for future research.
文摘Background: Compared to the general population, people who are at a high risk of or experience severe mental illness (SMI) such as psychosis, are more likely to have low levels of physical activity, high levels of sedentary behaviour, and sleep problems. Intervention: The Well-Track intervention comprises a wearable activity and sleep tracker (Fitbit);one session with mental health service staff providing physical activity and sleep hygiene advice;a brief motivational interview;completing a goal setting workbook;and one or two further engagement, feedback and discussion sessions. Participants: Twenty-four participants using an early intervention in psychosis (EIP) or at-risk mental state (ARMS) service completed an eight-week, three session intervention (14 males and 10 females), with an age range of 18 - 61, and average age of 27.75 years. Methods: An open-label patient cohort design with no control group. Pre-intervention, 4-week and 8-week intervention assessments using participant self-report measures: Patient Health Questionnaire (PHQ-9) (depression), Warwick–Edinburgh Mental Wellbeing Scale (WEMWBS), and Sleep Conditioning Index (SCI);and clinician measurement of body weight. Results: Mean scores showed a significant improvement in PHQ-9 from 9.29 (SD 5.89) to 5.58 (SD 3.68) at 4 weeks and to 5.83 (SD 4.40) at 8 weeks, with large effect sizes. For those who met a diagnosis of depression at baseline, at 4 week follow-up seven participants (26%) experienced remission and nine (33%) reliable improvement, and at 8 week follow-up four (21%) experienced remission and seven (37%) reliable improvement. WEMWBS scores significantly improved, from 44.04 (SD 9.44) to 48.54 (SD 8.71) at 4 weeks and to 48.67 (SD 8.76) at 8 weeks, with large effect sizes. Body weight did not change significantly, remaining unchanged at 4 weeks and reduced from a mean of 82.8 kg (baseline) to 80.15 kg at 8 weeks, a reduction of 2.65 kg. Conclusion: Well-Track was integrated into an EIP and ARMS service and was found to be beneficial in terms of wellbeing, depression, sleep, and preventing weight gain (either as a two or three engagement point intervention). Well-Track could be delivered through EIP and ARMS services to promote healthy lifestyle behaviours.
文摘Background: In psychosis physical activity, sleep, mental health, physical health, wellbeing, quality of life, cognition and functioning are interconnected. People who experience psychosis are more likely than the general population to have low levels of physical activity, high levels of sedentary behaviour and sleep problems. This project was innovative in seeking to address these issues through provision of a wearable activity and sleep tracker (a Fitbit) and sleep hygiene advice. Participants: Participants using an early intervention psychosis (EIP) service took part in an eight-week intervention, which incorporated the provision of a Fitbit, sleep hygiene advice as well as three engagement, feedback and discussion points with a clinician. Methods: A qualitative approach was used to conduct in-depth semi-structured interviews with 12 of the 25 intervention participants (5 male;7 female). Thematic and content analyses were employed to analyse the data. Results: Participants provided valuable insights into their experience of sleep, exercise, Fitbit use and sleep hygiene advice use. It was found that participants placed a high value on effective night time sleep, recognized improvements in physical activity and noted a positive effect on mood and wellbeing as a result of Fitbit use. The negative impact of having ineffective night time sleep and insufficient physical activity was described. Participants demonstrated a good level of understanding of the connection between sleep, exercise, wellbeing, and health. Conclusion: Participants reported the Fitbit and sleep hygiene advice received through an EIP service to be beneficial for improved levels of physical activity and exercise, and more effective sleep. This is a simple and low cost intervention which could be made widely available through EIP and other mental health services.
基金Supported by the National Key Basic Research and Development (973) Program of China (No. G2000077606 )
文摘A star identification algorithm was developed for a charge-coupled device （CCD） or complementary metal-oxide-semiconductor （CMOS） autonomous star tracker to acquire 3-axis attitude information for a lost-in-space spacecraft. The algorithm took advantage of an efficient on-board database and an original “4- star matching” pattern recognition strategy to achieve fast and reliable star identification. The on-board database was composed of a brightness independent guide star catalog （mission catalog） and a K-vector star pair catalog. The star pattern recognition method involved direct location of star pair candidates and a sim- ple array matching procedure. Tests of the algorithm with a CMOS active pixel sensor （APS） star tracker result in a 99.9% success rate for star identification for lost-in-space 3-axis attitude acquisition when the angular measurement accuracy of the star tracker is at least 0.01°. The brightness independent algorithm requires relatively higher measurement accuracy of the star apparent positions that can be easily achieved by CCD or CMOS sensors along with subpixel centroiding techniques.
