A theoretical framework to improve adherence among older adults to recommendations received at a falls prevention clinic: A narrative review

Santhagunam, Shree Nithi; Li, Eric P. H.; Buschert, Kim; Davis, Jennifer C.

doi:10.1016/j.apnr.2021.151493

Cited by 8 publications

(8 citation statements)

References 45 publications

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“…These findings differ from previous fall prevention studies where adherence ranged from 15% for physical activity to 45% for lifestyle modifications 31,32. These high levels of self-reported adherence to fall prevention actions may be due to incorporation of practices known to promote engagement, while addressing barriers to implementation including patient advisory council input, clinician-patient relationship, use of motivational interviewing, and patient control over their plan 6,7,33…”

Section: Discussioncontrasting

confidence: 79%

“…31,32 These high levels of self-reported adherence to fall prevention actions may be due to incorporation of practices known to promote engagement, while addressing barriers to implementation including patient advisory council input, clinician-patient relationship, use of motivational interviewing, and patient control over their plan. 6,7,33 Interprofessional care Moreover, an interprofessional approach was utilized. A surprisingly high number of patients were referred to physical therapy as part of the gait, strength, and balance FPOC.…”

Section: Discussionmentioning

confidence: 99%

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Implementing a Fall Prevention Initiative in Nurse-Facilitated Annual Wellness Visits

Quinlan

Ryer

2023

Journal of Nursing Care Quality

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Background: Falls are the leading cause of injury-related death and disability in community-dwelling older adults.Local Problem: Evidence-based fall prevention activities in primary care, including nurse-facilitated wellness visits, have been limited. Barriers including patient engagement and adherence exist. Method: A quality improvement project integrating components of the Centers for Disease Control and Prevention's Stopping Elder Accidents, Deaths & Injury (STEADI) was introduced by registered nurses during older adult annual wellness visits. Intervention: Nurses assessed risk and implemented patient-centered fall prevention plans including follow-up. Results: A total of 522 patients were screened, with 21% (n = 111) having increased fall risk. Of these, 78% (n = 87) engaged in home safety, gait, strength, and balance assessments and the majority (n = 83; 95%) participated in fall prevention plans of care. At 2-week follow-up, patients' self-reported adherence was 74% for gait/strength/balance and 67% for home safety. Conclusion: An expanded primary care team model shows promise for promoting fall prevention behaviors.

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Section: Discussioncontrasting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Implementing a Fall Prevention Initiative in Nurse-Facilitated Annual Wellness Visits

Quinlan

Ryer

2023

Journal of Nursing Care Quality

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“…Prior quantitative and qualitative research suggests older people’s engagement in evidence-based fall prevention strategies is essential to reduce their risks of falling 7 and begins to elucidate that engagement in fall prevention behaviors is a dynamic process. 8-11 For example, one recent study examined the extent to which older people engaged in fall prevention efforts using 3 indicators of patient engagement: (1) Consultation (receiving information), (2) Involvement (prioritizing risks), and (3) Partnership (identifying prevention actions) guided by Patient and Family Continuum of Engagement framework. 12 Participants engaged in a multifactorial assessment conducted by a registered nurse trained in fall prevention and motivational interviewing (MI) and discussed risks they prioritized.…”

Section: Gaps In Older Peoples’ Engagement In Fall Preventionmentioning

confidence: 99%

“…Prior quantitative and qualitative research suggests older people's engagement in evidence-based fall prevention strategies is essential to reduce their risks of falling 7 and begins to elucidate that engagement in fall prevention behaviors is a dynamic process. [8][9][10][11] For example, one recent study examined the extent to which older people engaged in fall prevention efforts using 3 indicators of patient engagement:…”

Section: Gaps In Older Peoples' Engagement In Fall Preventionmentioning

confidence: 99%

Older People’s Descriptions of Their Engagement in Fall Prevention

Kiyoshi-Teo,

McMahon,

Northup-Snyder

et al. 2023

West J Nurs Res

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Evidence-based strategies to decrease fall rates are well established. However, little is understood about how older people engage in fall prevention strategies. Motivational Interviewing (MI) sessions aimed to facilitate individuals’ engagement in fall prevention can be analyzed to learn what it means for older people to engage in fall prevention. Thus, the purpose of this study was to explore how older people describe their engagement in fall prevention. Participants in our parent project, MI for Fall Prevention (MI-FP), who received MI sessions were purposively selected for maximum variation in age, sex, fall risks, and MI specialist assigned. The first (of 8) MI sessions from 16 participants were recorded, transcribed, and analyzed using qualitative content analysis. Three researchers first deductively analyzed fall prevention strategies that participants described using an evidence-based fall prevention guideline as a reference. Then, we inductively analyzed the characteristics of these strategies and how participants engaged in them. Finally, we used the Capability, Opportunity, Motivation, Behavior (COM-B) model to organize our results about factors influencing engagement. We found (1) older adults engage in unique combinations of fall prevention strategies and (2) decisions about engagement in fall prevention strategies were influenced by multiple factors that were personal (e.g., who I am, capability, motivation, and opportunities). This study highlighted how fall prevention can be a life-long lifestyle decision for older people. Understanding older people’s perspectives about engaging in fall prevention is essential to develop interventions to promote evidence-based fall prevention strategies in real-world settings.

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“…Collaborative efforts aim to enhance the quality of life for seniors and alleviate the financial and healthcare burdens associated with fall-related injuries. However, it is essential to acknowledge the challenges faced by the elderly when it comes to wearing devices continuously [12]. The implications of elderly falls extend well beyond physical injuries, encompassing emotional, psychological, and societal dimensions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

Alharbi,

Hassan

2023

Sustainability

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In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

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A theoretical framework to improve adherence among older adults to recommendations received at a falls prevention clinic: A narrative review

Cited by 8 publications

References 45 publications

Implementing a Fall Prevention Initiative in Nurse-Facilitated Annual Wellness Visits

Implementing a Fall Prevention Initiative in Nurse-Facilitated Annual Wellness Visits

Older People’s Descriptions of Their Engagement in Fall Prevention

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

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