Objective: Self-management is a core theme within chronic care and several evidence-based interventions (EBIs) exist to promote self-management ability. However, these interventions cannot be adapted in a mere copy-paste manner. The current study describes and demonstrates a planned approach in adapting EBI’s in order to promote self-management in community-dwelling people with chronic conditions. Methods: We used Intervention Mapping (IM) to increase the intervention’s fit with a new context. IM helps researchers to take decisions about whether and what to adapt, while maintaining the working ingredients of existing EBI’s. Results: We present a case study in which we used IM to adapt EBI’s to the Flemish primary care context to promote self-management in people with one or more chronic disease. We present the reader with a contextual analysis, intervention aims, and content, sequence and scope of the resulting intervention. Conclusion: IM provides an excellent framework in providing detailed guidance on intervention adaption to a new context, while preserving the essential working ingredients of EBI’s. Practice Implications: The case study is exemplary for public health researchers and practitioners as a planned approach to seek and find EBI’s, and to make adaptations.
Pauses in speech may be categorized on the basis of their length. Some authors claim that there are two categories (short and long pauses) (Baken & Orlikoff, 2000), others claim that there are three (Campione & Véronis, 2002), or even more. Pause lengths may be affected in speakers with aphasia. Individuals with dementia probably caused by Alzheimer’s disease (AD) or Parkinson’s disease (PD) interrupt speech longer and more frequently. One infrequent form of dementia, non-fluent primary progressive aphasia (PPA-NF), is even defined as causing speech with an unusual interruption pattern (”hesitant and labored speech”). Although human listeners can often easily distinguish pathological speech from healthy speech, it is unclear yet how software can detect the relevant patterns. The research question in this study is: how can software measure the statistical parameters that characterize the disfluent speech of PPA-NF/AD/PD patients in connected conversational speech?
From the article: "Individuals with dementia often experience a decline in their ability to use language. Language problems have been reported in individuals with dementia caused by Alzheimer’s disease, Parkinson’s disease or degeneration of the fronto-temporal area. Acoustic properties are relatively easy to measure with software, which promises a cost-effective way to analyze larger discourses. We study the usefulness of acoustic features to distinguish the speech of German-speaking controls and patients with dementia caused by (a) Alzheimer’s disease, (b) Parkinson’s disease or (c) PPA/FTD. Previous studies have shown that each of these types affects speech parameters such as prosody, voice quality and fluency (Schulz 2002; Ma, Whitehill, and Cheung 2010; Rusz et al. 2016; Kato et al. 2013; Peintner et al. 2008). Prior work on the characteristics of the speech of individuals with dementia is usually based on samples from clinical tests, such as the Western Aphasia Battery or the Wechsler Logical Memory task. Spontaneous day-to-day speech may be different, because participants may show less of their vocal abilities in casual speech than in specifically designed test scenarios. It is unclear to what extent the previously reported speech characteristics are still detectable in casual conversations by software. The research question in this study is: how useful for classification are acoustic properties measured in spontaneous speech."
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