BACKGROUND: Ambulatory children with Spina Bifida (SB) often show a decline in physical activity leading to deconditioning and functional decline. Therefore, assessment and promotion of physical activity is important. Because energy expenditure during activities is higher in these children, the use of existing pediatric equations to predict physical activity energy expenditure (PAEE) may not be valid. AIMS: (1) To evaluate criterion validity of existing predictions converting accelerocounts into PAEE in ambulatory children with SB and (2) to establish new disease-specific equations for PAEE. METHODS: Simultaneous measurements using the Actical, the Actiheart, and indirect calorimetry took place to determine PAEE in 26 ambulatory children with SB. DATA ANALYSIS: Paired T-tests, Intra-class correlations limits of agreement (LoA), and explained variance (R2) were used to analyze validity of the prediction equations using true PAEE as criterion. New equations were derived using regression techniques. RESULTS: While T-tests showed no significant differences for some models, the predictions developed in healthy children showed moderate ICC’s and large LoA with true PAEE. The best regression models to predict PAEE were: PAEE = 174.049 + 3.861 × HRAR – 60.285 × ambulatory status (R2 = 0.720) and PAEE = 220.484 + 0.67 × Actical counts – 60.717 × ambulatory status (R2 = 0.681). CONCLUSIONS: Existing equations to predict PAEE are not valid for use in children with SB for the individual evaluation of PAEE. The best regression model was based on HRAR in combination with ambulatory status, followed by a new model for the Actical monitor. A benefit of HRAR is that it does not require the use of expensive accelerometry equipment. Further cross-validation of these models is still needed.
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Presentatie gegeven tijdens het congres Supporting Health by Technology (12-13 may - Groningen)
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OBJECTIVES: To study sensor monitoring (use of a sensor network placed in the home environment to observe individuals' daily functioning (activities of daily living and instrumental activities of daily living)) as a method to measure and support daily functioning for older people living independently at home.DESIGN: Systematic review.SETTING: Participants' homes.PARTICIPANTS: Community-dwelling individuals aged 65 and older.MEASUREMENTS: A systematic search in PubMed, Embase, PsycINFO, INSPEC, and The Cochrane Library was performed for articles published between 2000 and October 2012. All study designs, studies that described the use of wireless sensor monitoring to measure or support daily functioning for independently living older people, studies that included community-dwelling individuals aged 65 and older, and studies that focused on daily functioning as a primary outcome measure were included.RESULTS: Seventeen articles met the inclusion criteria. Nine studies used sensor monitoring solely as a method for measuring daily functioning and detecting changes in daily functioning. These studies focused on the technical investigation of the sensor monitoring method used. The other studies investigated clinical applications in daily practice. The sensor data could enable healthcare professionals to detect alert conditions and periods of decline and could enable earlier intervention, although limited evidence of the effect of interventions was found in these studies because of a lack of high methodological quality.CONCLUSION: Studies on the effectiveness of sensor monitoring to support people in daily functioning remain scarce. A road map for further development is proposed.
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Physical activity monitoring with wearable technology has the potential to support stroke rehabilitation. Little is known about how physical therapists use and value the use of wearable activity monitors. This cross-sectional study explores the use, perspectives, and barriers to wearable activity monitoring in day-to-day stroke care routines amongst physical therapists. Over 300 physical therapists in primary and geriatric care and rehabilitation centers in the Netherlands were invited to fill in an online survey that was developed based on previous studies and interviews with experts. In total, 103 complete surveys were analyzed. Out of the 103 surveys, 27% of the respondents were already using activity monitoring. Of the suggested treatment purposes of activity monitoring, 86% were perceived as useful by more than 55% of the therapists. The most recognized barriers to clinical implementation were lack of skills and knowledge of patients (65%) and not knowing what brand and type of monitor to choose (54%). Of the non-users, 79% were willing to use it in the future. In conclusion, although the concept of remote activity monitoring was perceived as useful, it was not widely adopted by physical therapists involved in stroke care. To date, skills, beliefs, and attitudes of individual therapists determine the current use of wearable technology.
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Background: The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. Objective: This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. Methods: In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. Results: Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). Conclusions: To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes.
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This thesis aims to develop and validate a comprehensive and adaptable activity monitoring system that quantifies physical behaviours in children with and without developmental disabilities, including those utilizing assistive devices. This system seeks to overcome the current limitations in the accuracy and feasibility of existing monitoring devices by providing robust measurements in real-world settings.
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Abstract Background Smoking among people with severe mental illness (SMI) is highly prevalent and strongly associated with poor physical health. Currently, evidence-based smoking cessation interventions are scarce and need to be integrated into current mental health care treatment guidelines and clinical practice. Therefore, the present study aims to evaluate the implementation and effectiveness of a smoking cessation intervention in comparison with usual care in people with SMI treated by Flexible Assertive Community Treatment (FACT) teams in the Netherlands. Methods A pragmatic, cluster-randomised controlled trial with embedded process evaluation will be conducted. Randomisation will be performed at the level of FACT teams, which will be assigned to the KISMET intervention or a control group (care as usual). The intervention will include pharmacological treatment combined with behavioural counselling and peer support provided by trained mental health care professionals. The intervention was developed using a Delphi study, through which a consensus was reached on the core elements of the intervention. We aim to include a total of 318 people with SMI (aged 18–65 years) who smoke and desire to quit smoking. The primary outcome is smoking status, as verified by carbon monoxide measurements and self-report. The secondary outcomes are depression and anxiety, psychotic symptoms, physical fitness, cardiovascular risks, substance use, quality of life, and health-related self-efficacy at 12 months. Alongside the trial, a qualitative process evaluation will be conducted to evaluate the barriers to and facilitators of its implementation as well as the satisfaction and experiences of both patients and mental health care professionals. Discussion The results of the KISMET trial will contribute to the evidence gap of effective smoking cessation interventions for people treated by FACT teams. Moreover, insights will be obtained regarding the implementation process of the intervention in current mental health care. The outcomes should advance the understanding of the interdependence of physical and mental health and the gradual integration of both within the mental health care system. Trial registration Netherlands Trial Register, NTR9783. Registered on 18 October 2021.
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Abstract Background Smoking among people with severe mental illness (SMI) is highly prevalent and strongly associated with poor physical health. Currently, evidence-based smoking cessation interventions are scarce and need to be integrated into current mental health care treatment guidelines and clinical practice. Therefore, the present study aims to evaluate the implementation and efectiveness of a smoking cessation intervention in comparison with usual care in people with SMI treated by Flexible Assertive Community Treatment (FACT) teams in the Netherlands. Methods A pragmatic, cluster-randomised controlled trial with embedded process evaluation will be conducted. Randomisation will be performed at the level of FACT teams, which will be assigned to the KISMET intervention or a control group (care as usual). The intervention will include pharmacological treatment combined with behavioural counselling and peer support provided by trained mental health care professionals. The intervention was developed using a Delphi study, through which a consensus was reached on the core elements of the intervention. We aim to include a total of 318 people with SMI (aged 18–65 years) who smoke and desire to quit smoking. The primary outcome is smoking status, as verifed by carbon monoxide measurements and self-report. The secondary outcomes are depression and anxiety, psychotic symptoms, physical ftness, cardiovascular risks, substance use, quality of life, and health-related self-efcacy at 12months. Alongside the trial, a qualitative process evaluation will be conducted to evaluate the barriers to and facilitators of its implementation as well as the satisfaction and experiences of both patients and mental health care professionals. Discussion The results of the KISMET trial will contribute to the evidence gap of efective smoking cessation interventions for people treated by FACT teams. Moreover, insights will be obtained regarding the implementation process of the intervention in current mental health care. The outcomes should advance the understanding of the interdependence of physical and mental health and the gradual integration of both within the mental health care system. Trial registration Netherlands Trial Register, NTR9783. Registered on 18 October 2021.
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Community-dwelling stroke survivors tend to become less physically active over time. There is no ‘gold standard’ to measure walking activity in this population. Assessment of walking activity generally involves subjective or observer-rated instruments. Objective measuring with an activity monitor, however, gives more insight into actual walking activity. Although several activity monitors have been used in stroke patients, none of these include feedback about the actual walking activity. FESTA (FEedback to Stimulate Activity) determines number of steps, number of walking bouts, covered distance and ambulatory activity profiles over time and also provides feedback about the walking activity to the user and the therapist.
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Maintaining mental health can be quite challenging, especially when exposed to stressful situations. In many cases, mental health problems are recognized too late to effectively intervene and prevent adverse outcomes. Recent advances in the availability and reliability of wearable technologies offer opportunities for continuously monitoring mental states, which may be used to improve a person’s mental health. Previous studies attempting to detect and predict mental states with different modalities have shown only small to moderate effect sizes. This limited success may be due to the large variability between individuals regarding e.g., ways of coping with stress or behavioral patterns associated with positive or negative feelings. A study was set up for the detection of mental states based on longitudinal wearable and contextual sensing, targeted at investigating between-subjects variations in terms of predictors of mental states and variations in how predictors relate to mental states. At the end of March 2022, 16 PhD candidates from the Netherlands started to participate in the study. Over nine months, we collected data in terms of their daily mental states (valence and arousal), continuous physiological data (Oura ring) and smartphone data (AWARE framework including GPS and smartphone usage). From the raw data, we aggregated daily values for each participant in terms of sleep, physical activity, mental states, phone usage and GPS movement. First results (six months into the study at the time of writing) indicate that almost all participants show a large variability in ratings of daily mental states, which is a prerequisite for predictive modeling. Direction, strength and standard deviations of Spearman correlations between valence, arousal and the different variables suggest that several predictors of valence and arousal are more subject dependent than others. In future analyses, we will test and compare different versions of predictive modeling to highlight the potential of wearable technologies for mental state monitoring and the personalized prediction of the development of mental problems.
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