Background: Most studies on older adults' vitality focus on linear connections between determinants and outcomes. To design more comprehensive and impactful approaches to support the vitality of older adults, a better understanding of the interplay among elements that shape their vitality is necessary. Objective: To uncover the underlying dynamic system that drives vitality in older adults, drawing directly from older adults' perspectives. Methods: During three group model-building sessions with 10–12 older adults (≥ 55 years old), a causal loop diagram with relevant feedback loops was developed through co-creation with older adults. The construction and analysis of the causal loop diagram were facilitated using the online modelling tools Vensim and Kumu. The group model-building sessions were guided by Scriptapedia, an online guide to conducting group model-building practices. Results: The final CLD consisted of 15 elements contributing to older adults' vitality, organised into three themes: ‘Psychological and emotional elements’, ‘Social connections and support’ and ‘Lifestyle and habits’. A total of 41 reinforcing feedback loops were identified, with 21 involving all three themes, 15 connecting two themes and 5 within a single theme. Conclusions: This study displays the complex interplay of elements influencing older adults' vitality, highlighting the critical roles of psychological, social and lifestyle-related elements. The participatory-led approach yielded co-produced insights that inform public health strategies, underscoring the need for comprehensive, multidisciplinary approaches to promote older adults' vitality. Such approaches must offer social opportunities and foster individuals' capacity and motivation to engage in meaningful social relationships. Patient or Public Contribution: The study was conducted in collaboration with a municipal policymaker and a coordinator of local community centres, who provided input on participant recruitment, materials, data interpretation, ethical considerations and result dissemination. During data collection, twelve older adults participated in three group model-building sessions, collaboratively developing a causal loop diagram to explore elements of vitality and their interconnections. Ongoing member checking with participants throughout the process ensured the analysis was refined and the researchers' interpretations were validated.
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Just what and how eight experienced teachers in four coaching dyads learned during a 1-year reciprocal peer coaching trajectory was examined in the present study. The learning processes were mapped by providing a detailed description of reported learning activities, reported learning outcomes, and the relations between these two. The sequences of learning activities associated with a particular type of learning outcome were next selected, coded, and analyzed using a variety of quantitative methods. The different activity sequences undertaken by the teachers during a reciprocal peer coaching trajectory were found to trigger different aspects of their professional development.
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To assess the reporting quality of interventions aiming at promoting physical activity (PA) using a wearable activity tracker (WAT) in patients with infammatory arthritis (IA) or hip/knee osteoarthritis (OA). A systematic search was performed in eight databases including PubMed, Embase and Cochrane Library) for studies published between 2000 and 2022. Two reviewers independently selected studies and extracted data on study characteristics and the reporting of the PA intervention using a WAT using the Consensus on Exercise reporting Template (CERT) (12 items) and Consolidated Standards of Reporting Trials (CONSORT) E-Health checklist (16 items). The reporting quality of each study was expressed as a percentage of reported items of the total CERT and CONSORT E-Health (50% or less=poor; 51–79%=moderate; and 80–100%=good reporting quality). Sixteen studies were included; three involved patients with IA and 13 with OA. Reporting quality was poor in 6/16 studies and moderate in 10/16 studies, according to the CERT and poor in 8/16 and moderate in 8/16 studies following the CONSORT E-Health checklist. Poorly reported checklist items included: the description of decision rule(s) for determining progression and the starting level, the number of adverse events and how adherence or fdelity was assessed. In clinical trials on PA interventions using a WAT in patients with IA or OA, the reporting quality of delivery process is moderate to poor. The poor reporting quality of the progression and tailoring of the PA programs makes replication difcult. Improvements in reporting quality are necessary.
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Aim: The aim of this study was to describe the experience with commercially available activity trackers embedded in the physiotherapy treatment of patients with a chronic disease. Methods: In a qualitative study, 29 participants with a chronic disease participated. They wore an activity tracker for two to eight weeks. Data were collected using 23 interviews and discussion with 6 participants. A framework analysis was used to analyze the data. Results: The framework analysis resulted in seven categories: purchase, instruction, characteristics, correct functioning, sharing data, privacy, use, and interest in feedback. The standard goal of the activity trackers was experienced as too high, however the tracker still motivated them to be more active. Participants would have liked more guidance from their physiotherapists because they experienced the trackers as complex. Participants experienced some technical failures, are willing to share data with their physiotherapist and, want to spend a maximum of €50,-. Conclusion: The developed framework gives insight into all important concepts from the experiences reported by patients with a chronic disease and can be used to guide further research and practice. Patients with a chronic disease were positive regarding activity trackers in general. When embedded in physiotherapy, more attention should be paid to the integration in treatment.
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Aim: The aim of this study was to describe the experience with commercially available activity trackers embedded in the physiotherapy treatment of patients with a chronic disease. Methods: In a qualitative study, 29 participants with a chronic disease participated. They wore an activity tracker for two to eight weeks. Data were collected using 23 interviews and discussion with 6 participants. A framework analysis was used to analyze the data. Results: The framework analysis resulted in seven categories: purchase, instruction, characteristics, correct functioning, sharing data, privacy, use, and interest in feedback. The standard goal of the activity trackers was experienced as too high, however the tracker still motivated them to be more active. Participants would have liked more guidance from their physiotherapists because they experienced the trackers as complex. Participants experienced some technical failures, are willing to share data with their physiotherapist and, want to spend a maximum of e50,-. Conclusion: The developed framework gives insight into all important concepts from the experiences reported by patients with a chronic disease and can be used to guide further research and practice. Patients with a chronic disease were positive regarding activity trackers in general. When embedded in physiotherapy, more attention should be paid to the integration in treatment.
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Background: Osteoarthritis (OA) is a chronic disease primarily affecting older adults, mainly impacting the hip and knee joints. The increasing prevalence of OA contributes to rising healthcare demands and costs. Current OA treatment guidelines emphasize the importance of self-management education and guidance, particularly in promoting physical activity and weight management. In addition, improving sleep is crucial for managing OA. Developing effective self-management interventions necessitates a comprehensive understanding of the factors that facilitate these behaviors. Especially for changing health behaviors, it is important to focus on psychosocial factors. Therefore, this systematic review aimed to identify the psychosocial factors associated with physical activity, weight management, and sleep in adults with hip and/or knee OA. Methods: Five databases (PubMed, Embase, CINAHL, PyschINFO, Web of Science) were searched for observational studies reporting statistics on the association between psychosocial determinants and physical activity, weight management, or sleep in people with OA. The methodological quality was assessed using the Quality Assessment Tool for Observational Studies of the National Heart, Lung, and Blood Institute. After screening 5,812 articles, 31 studies were included for analysis. Results: The results showed that intention, self-efficacy, and willpower beliefs were positively associated with physical activity. Kinesiophobia, pain catastrophizing and pain-related fear were negatively associated with physical activity. Depressive symptoms, negative affect, pain catastrophizing, and low willpower beliefs were associated with poor weight management. Anxiety, depression, pain anxiety, and post-traumatic stress disorder were related to poor sleep behavior. Conclusions This review enhances the understanding of the psychosocial factors underlying physical activity, weight management and sleep in OA. These insights are valuable for developing tailored behavior change interventions aimed at improving physical activity, weight management and sleep in patients with hip and/or knee OA.
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mHealth 24/7 is een dienst die diabetespatiënten helpt om op eenvoudige wijze toezicht te houden op hun eigen gezondheid. mDiabetes 24/7 is een prototype app binnen de dienst mHealth 24/7. Op dit moment kunnen patiënten met het prototype van de app hun bloedsuikerwaardes, een eetdagboek en de hoeveelheid toegediende insuline bijhouden. mHealth 24/7 heeft de wens geformuleerd om haar informatievoorziening aan diabetespatiënten verder uit te breiden, door gepersonaliseerd inzicht te geven in de oorzaak van stijgingen en dalingen van hun bloedsuikerwaarden. Meer informatie stelt de patiënt in staat om beter gemotiveerde maatregelen te nemen en stimuleert therapietrouw waarmee later complicaties kunnen worden voorkomen. Dit verbetert de kwaliteit van leven en vermindert kosten.In het project is gerealiseerd dat data uit een activity tracker en omgevingstemperatuur ingelezen wordt in de app en wordt geïntegreerd met bestaande data zoals bloedsuikerwaarde. Daarnaast kunnen patiënten handmatig aangeven hoe ze zich voelen. Patiënten krijgen daarmee inzicht in het effect van activiteit, omgevingstemperatuur en stemming op fluctuaties in bloedsuikerwaardes. In een pilot met 25 proefpersonen is de technische werking van de verrijkte app getest evenals de functionaliteit.Er is aangetoond dat de app werkt en dat voor gebruikers de verrijking van de informatie in de app met hartslag, omgevingstemperatuur en stemming van toegevoegde waarde is. Wel blijkt dat een app zoals deze foutloos en realtime moet werken en de gebruiksinterface dusdanig moet werken, dat de gebruikers er uitsluitend gemak van ondervinden. Diabetes is een arbeidsintensieve ziekte en nog meer werk is ongewenst!Als in een volgende pilot meer data kan worden verzameld, kan worden gewerkt aan het voorspellen van fluctuaties in bloedsuikerwaardes waardoor een patiënt ook voortijdig gewaarschuwd kan worden.Vanuit verschillende marktpartijen zoals ziekenhuizen en zorgverzekeraars is interesse getoond voor het project. Gezamenlijk gaan deze partijen aanspraak doen op tijdelijke financiering vanuit de “Beleidsregel Innovatie Kleinschalige Experimenten.
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Study selection: Randomized controlled trials published after 2007 with (former) healthcare patients ≥ 21 years of age were included if physical activity was measured objectively using a wearable monitor for both feedback and outcome assessment. The main goal of included studies was promoting physical activity. Any concurrent strategies were related only to promoting physical activity. Data extraction: Effect sizes were calculated using a fixed-effects model with standardized mean difference. Information on study characteristics and interventions strategies were extracted from study descriptions. Data synthesis: Fourteen studies met the inclusion criteria (total n = 1,902), and 2 studies were excluded from meta-analysis. The overall effect size was in favour of the intervention groups (0.34, 95% CI 0.23–0.44, p < 0.01). Study characteristics and intervention strategies varied widely. Conclusion: Healthcare interventions using feedback on objectively monitored physical activity have a moderately positive effect on levels of physical activity. Further research is needed to determine which strategies are most effective to promote physical activity in healthcare programmes. Lay Abstract Wearable technology is progressively applied in health care and rehabilitation to provide objective insight into physical activity levels. In addition, feedback on physical activity levels delivered by wearable monitors might be beneficial for optimizing their physical activity. A systematic review and meta-analysis was conducted to evaluate the effectiveness of interventions using feedback on objectively measured physical activity in patient populations. Fourteen studies including 1902 patients were analyzed. Overall, the physical activity levels of the intervention groups receiving objective feedback on physical activity improved, compared to the control groups receiving no objective feedback. Mostly, a variety of other strategies were applied in the interventions next to wearable technology. Together with wearable technology, behavioral change strategies, such as goal-setting and action planning seem to be an important ingredient to promote physical activity in health care and rehabilitation. LinkedIn: https://www.linkedin.com/in/hanneke-braakhuis-b9277947/ https://www.linkedin.com/in/moniqueberger/
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BACKGROUND: Although enhancing physical activity (PA) is important to improve physical and/or cognitive recovery, little is known about PA of patients admitted to an inpatient rehabilitation setting. Therefore, this study assessed the quantity, nature and context of inpatients PA admitted to a rehabilitation center. METHODOLOGY/PRINICIPAL FINDINGS: Prospective observational study using accelerometry & behavioral mapping. PA of patients admitted to inpatient rehabilitation was measured during one day between 7.00-22.00 by means of 3d-accelerometery (Activ8; percentage of sedentary/active time, number of sedentary/active bouts (continuous period of ≥1 minute), and active/sedentary bout lengths and behavioral mapping. Behavioral mapping consisted of observations (every 20 minutes) to assess: type of activity, body position, social context and physical location. Descriptive statistics were used to describe PA on group and individual level. At median the 15 patients spent 81% (IQR 74%-85%) being sedentary. Patients were most sedentary in the evening (maximum sedentary bout length minutes of 69 (IQR 54-95)). During 54% (IQR 50%-61%) of the observations patients were alone) and in their room (median 50% (IQR 45%-59%)), but individual patterns varied widely. CONCLUSION/SIGNIFICANCE: The results of this study enable a deeper understanding of the daily PA patterns of patients admitted for inpatient rehabilitation treatment. PA patterns of patients differ in both quantity, day structure, social and environmental contexts. This supports the need for individualized strategies to support PA behavior during inpatient rehabilitation treatment.
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Background: This follow-up study investigated the year-round effects of a four-week randomized controlled trial using different types of feedback on employees’ physical activity, including a need-supportive coach intervention. Methods: Participants (n=227) were randomly assigned to a Minimal Intervention Group (MIG; no feedback), a Pedometer Group (PG; feedback on daily steps only), a Display Group (DG; feedback on daily steps, on daily moderateto-vigorous physical activity [MVPA] and on total energy expenditure [EE]), or a Coaching Group (CoachG; same as DG with need supportive coaching). Daily physical activity level (PAL; Metabolic Equivalent of Task [MET]), number of daily steps, daily minutes of moderate to vigorous physical activity (MVPA), active daily EE (EE>3 METs) and total daily EE were measured at five time points: before the start of the 4-week intervention, one week after the intervention, and 3, 6, and 12 months after the intervention. Results: For minutes of MVPA, MIG showed higher mean change scores compared with the DG. For steps and daily minutes of MVPA, significantly lower mean change scores emerged for MIG compared with the PG. Participants of the CoachG showed significantly higher change scores in PAL, steps, minutes of MVPA, active EE, total EE compared with the MIG. As hypothesized, participants of the CoachG had significantly higher mean change scores in PAL and total EE compared with groups that only received feedback. However, no significant differences were found for steps, minutes of MVPA and active EE between CoachG and PG. Conclusions: Receiving additional need-supportive coaching resulted in a higher PAL and active EE compared with measurement (display) feedback only. These findings suggest to combine feedback on physical activity with personal coaching in order to facilitate long-term behavioral change. When it comes to increasing steps, minutes of MVPA or active EE, a pedometer constitutes a sufficient tool. Trial registration: Clinical Trails.gov NCT01432327. Date registered: 12 September 2011
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