Arm hand skilled performance in cerebral palsy: activity preferences and their movement components

Background: Assessment of arm-hand use is very important in children with cerebral palsy (CP) who encounter arm-hand problems. To determine validity and reliability of new instruments to assess actual performance, a set of standardized test situations including activities of daily living (ADL) is required. This study gives information with which such a set for upper extremity skill research may be fine-tuned, relative to a specific research question. Aim of this study is to a) identify upper extremity related ADL children with CP want to improve on, b) determine the 10 most preferred goals of children with CP, and c) identify movement components of all goals identified. Method: The Canadian Occupational Performance Measure was used to identify upper extremity-related ADL preferences (goals) of 53 children with CP encountering arm-hand problems (mean age 9 +/- 4.5 year). Goals were ranked based on importance attributed to each goal and the number of times a goal was mentioned, resulting in a gross list with goals. Additionally, two studies were performed, i.e. study A to determine the 10 most preferred goals for 3 age groups (2.5-5 years; 6-11 years, 12-19 years), based on the total preference score, and study B to identify movement components, like reaching and grasping, of all goals identified for both the leading and the assisting arm-hand. Results: Seventy-two goals were identified. The 10 most preferred goals differed with age, changing from dressing and leisure-related goals in the youngest children to goals regarding personal care and eating for children aged 6-11 years. The oldest children preferred goals regarding eating, personal care and computer use. The movement components 'positioning', 'reach', 'grasp', and 'hold' were present in most tasks. 'Manipulating' was more important for the leading arm-hand, whereas 'fixating' was more important for the assisting arm-hand. Conclusion: This study gave insight into the preferences regarding ADL children with CP would like to improve on, and the movement components characterizing these activities. This information can be used to create a set of standardized test situations, which can be used to assess the validity and reliability of new measurement instruments to gauge actual arm-hand skilled performance

Promoting physical activity and a healthy active lifestyle in community-dwelling older adults: a design thinking approach for the development of a mobile health application

BackgroundPhysical activity (PA) has wide-ranging, and well documented benefits for older adults, encompassing physical, cognitive, and mental well-being. The World Health Organization advocates for a minimum of 150–300 min of moderate intensity PA per week, supplemented by muscle-strengthening exercises. However, the rates of PA among older adults remain a concern. While portable technologies hold promises in promoting PA, sustaining long-term engagement continues to be a challenge.ObjectiveThe aims of this study are to identify barriers and facilitators to PA in older adults, to develop an mHealth app promoting PA and an active healthy lifestyle in collaboration with community-dwelling older adults guided by the design thinking process, and to test it.MethodsA co-creative process was used, employing design thinking. Interviews were conducted to understand the needs of the target population and identify the problem of insufficient PA. Two cocreation sessions involving older adults and experts were conducted to generate innovative ideas. Participants were selected based on age (≥65 years), no severe illness, Dutch language proficiency, and active participation ability. Results were qualitatively analyzed and coded. Finally a prototype was developed and tested.ResultsInterviews with older adults highlighted diverse perceptions of PA but unanimous agreement on its importance. They recognized health benefits such as improved mobility, balance, and reduced fall risk, while emphasizing the social and mental aspects. Barriers included poor health, time constraints, weather conditions and fear of falling. Cocreation sessions identified key topics: perception of a healthy lifestyle, coping strategies, mHealth App features, screen visualization, and tailored notifications, which led to the development of a mobile app promoting PA and an active lifestyle. The app was stepwise prototyped.ConclusionThis study emphasizes the importance of promoting PA among older adults through a collaborative design thinking approach. However, the implementation of mHealth apps faces obstacles due to the digital divide, necessitating personalized solutions to bridge the gap. Moreover, it calls for further research to investigate the long-term impact of such interventions and explore behavior change patterns in this population

Valid and reliable instruments for arm-hand assessment at ICF activity level in persons with hemiplegia: a systematic review

Contains fulltext : 110141.pdf (publisher's version ) (Open Access)BACKGROUND: Loss of arm-hand performance due to a hemiparesis as a result of stroke or cerebral palsy (CP), leads to large problems in daily life of these patients. Assessment of arm-hand performance is important in both clinical practice and research. To gain more insight in e.g. effectiveness of common therapies for different patient populations with similar clinical characteristics, consensus regarding the choice and use of outcome measures is paramount. To guide this choice, an overview of available instruments is necessary. The aim of this systematic review is to identify, evaluate and categorize instruments, reported to be valid and reliable, assessing arm-hand performance at the ICF activity level in patients with stroke or cerebral palsy. METHODS: A systematic literature search was performed to identify articles containing instruments assessing arm-hand skilled performance in patients with stroke or cerebral palsy. Instruments were identified and divided into the categories capacity, perceived performance and actual performance. A second search was performed to obtain information on their content and psychometrics. RESULTS: Regarding capacity, perceived performance and actual performance, 18, 9 and 3 instruments were included respectively. Only 3 of all included instruments were used and tested in both patient populations. The content of the instruments differed widely regarding the ICF levels measured, assessment of the amount of use versus the quality of use, the inclusion of unimanual and/or bimanual tasks and the inclusion of basic and/or extended tasks. CONCLUSIONS: Although many instruments assess capacity and perceived performance, a dearth exists of instruments assessing actual performance. In addition, instruments appropriate for more than one patient population are sparse. For actual performance, new instruments have to be developed, with specific focus on the usability in different patient populations and the assessment of quality of use as well as amount of use. Also, consensus about the choice and use of instruments within and across populations is needed

Valid and reliable instruments for arm-hand assessment at ICF activity level in persons with hemiplegia: a systematic review

Background: Loss of arm-hand performance due to a hemiparesis as a result of stroke or cerebral palsy (CP), leads to large problems in daily life of these patients. Assessment of arm-hand performance is important in both clinical practice and research. To gain more insight in e. g. effectiveness of common therapies for different patient populations with similar clinical characteristics, consensus regarding the choice and use of outcome measures is paramount. To guide this choice, an overview of available instruments is necessary. The aim of this systematic review is to identify, evaluate and categorize instruments, reported to be valid and reliable, assessing arm-hand performance at the ICF activity level in patients with stroke or cerebral palsy. Methods: A systematic literature search was performed to identify articles containing instruments assessing arm-hand skilled performance in patients with stroke or cerebral palsy. Instruments were identified and divided into the categories capacity, perceived performance and actual performance. A second search was performed to obtain information on their content and psychometrics. Results: Regarding capacity, perceived performance and actual performance, 18, 9 and 3 instruments were included respectively. Only 3 of all included instruments were used and tested in both patient populations. The content of the instruments differed widely regarding the ICF levels measured, assessment of the amount of use versus the quality of use, the inclusion of unimanual and/or bimanual tasks and the inclusion of basic and/or extended tasks. Conclusions: Although many instruments assess capacity and perceived performance, a dearth exists of instruments assessing actual performance. In addition, instruments appropriate for more than one patient population are sparse. For actual performance, new instruments have to be developed, with specific focus on the usability in different patient populations and the assessment of quality of use as well as amount of use. Also, consensus about the choice and use of instruments within and across populations is needed

Placement of the sensor devices.

<p>A) placement on the dominant arm-hand and chest of healthy participants, B) bilateral placement on the arms, hands and chest of the stroke patient, C) top view of the placement of the sensor devices on the hand.</p

Identification of the activities ‘drinking’, ‘eating’ and ‘brushing hair’ in a sequence of recordings of multiple standardized daily activities of a stroke patient.

<p>Panel A displays the superimposed signals (63 in total) of a recording of a stroke patient while performing three tasks, namely drinking from a cup, eating with knife and fork and brushing hair. Pattern recognition using an individual template is shown for the tasks ‘drinking’ (Panel B), ‘eating’ (Panel C) and ‘brushing hair’ (Panel D). Pattern recognition with the complete task as template and with the task sub-phases as template are both shown. The black lines (complete task) and coloured lines (task sub-phases) in panels B and C mark the places were the template is recognised in the longer recording.</p

Schematic overview of the procedure of pattern recognition (1 of the 36 signals is shown).

<p>A) Systematic variation in template window length using linear interpolation, example given of 3 of the in total 17 variations in window length, B) Pattern recognition based on 2D convolution, performed for every template window separately, C) Cross-correlation coefficient functions, calculated during pattern recognition, example given of 3 of the in total 17 cross-correlation coefficient functions. Local maxima were identified per cross-correlation coefficient function (marked with red circle) and local maxima with highest cross-correlation of all cross-correlation functions were identified (marked with red square), D) Overview of epochs where the template is recognized, including start and endpoints (in data points, DP), cross-correlation coefficients and relative window length. Red lines mark the places in the sequence of recordings of multiple standardized daily activities were the template is recognized. DP = data point.</p

Identification of the activity ‘drinking’ in a sequence of recordings of multiple standardized daily activities of a healthy participant.

<p>Panel A displays the superimposed signals (36 in total) of a recording of an individual while executing three tasks, namely drinking from a cup, eating with knife and fork and brushing hair. Panel B displays the pattern recognition using an individual template and the pattern recognition using a generic template consisting of the mean signal of 30 persons. Both pattern recognition with the complete task as template and task sub-phases as template are shown. The black lines (complete task) and coloured lines (task sub-phases) in Panel B mark the places were the template is recognised in the longer recording.</p

Identification of sub-phases of a task, based on modeling the summed gyroscope signal with non-normalized Gaussian curves.

<p>R² displays the amount of signal variance explained.</p