Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 19 de 19
Filtrar
1.
JMIR Med Inform ; 8(11): e19805, 2020 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-33200991

RESUMO

BACKGROUND: The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. OBJECTIVE: This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans. METHODS: In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons. RESULTS: DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy). CONCLUSIONS: Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

2.
Int J Med Inform ; 145: 104324, 2020 Nov 02.
Artigo em Inglês | MEDLINE | ID: mdl-33181446

RESUMO

BACKGROUND: Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions. OBJECTIVE: This paper empirically analyses whether text in medical discharge reports follow Zipf's law, a commonly assumed statistical property of language where word frequency follows a discrete power-law distribution. METHOD: We examined 20,000 medical discharge reports from the MIMIC-III dataset. Methods included splitting the discharge reports into tokens, counting token frequency, fitting power-law distributions to the data, and testing whether alternative distributions-lognormal, exponential, stretched exponential, and truncated power-law-provided superior fits to the data. RESULT: Discharge reports are best fit by the truncated power-law and lognormal distributions. Discharge reports appear to be near-Zipfian by having the truncated power-law provide superior fits over a pure power-law. CONCLUSION: Our findings suggest that Bayesian modelling and statistical text analysis of discharge report text would benefit from using truncated power-law and lognormal probability priors and non-parametric models that capture power-law behavior.

3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2155-2158, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018433

RESUMO

Exercising has various health benefits and it has become an integral part of the contemporary lifestyle. However, some workouts are complex and require a trainer to demonstrate their steps. Thus, there are various workout video tutorials available online. Having access to these, people are able to independently learn to perform these workouts by imitating the poses of the trainer in the tutorial. However, people may injure themselves if not performing the workout steps accurately. Therefore, previous work suggested to provide visual feedback to users by detecting 2D skeletons of both the trainer and the learner, and then using the detected skeletons for pose accuracy estimation. Using 2D skeletons for comparison may be unreliable, due to the highly variable body shapes, which complicate their alignment and pose accuracy estimation. To address this challenge, we propose to estimate 3D rather than 2D skeletons and then measure the differences between the joint angles of the 3D skeletons. Leveraging recent advancements in deep latent variable models, we are able to estimate 3D skeletons from videos. Furthermore, a positive-definite kernel based on diversity-encouraging prior is introduced to provide a more accurate pose estimation. Experimental results show the superiority of our proposed 3D pose estimation over the state-of-the-art baselines.

4.
Artigo em Inglês | MEDLINE | ID: mdl-33017931

RESUMO

Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.


Assuntos
Ondas Encefálicas , Eletroencefalografia , Encéfalo/diagnóstico por imagem , Aprendizado de Máquina , Personalidade
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 545-548, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018047

RESUMO

The use of feature extraction and selection from EEG signals has shown to be useful in the detection of epileptic seizure segments. However, these traditional methods have more recently been surpassed by deep learning techniques, forgoing the need for complex feature engineering. This work aims to extend the conventional approach of epileptic seizure detection utilizing raw power spectra of EEG signals and convolutional neural networks (CNN). The proposed technique utilizes wavelet transform to compute the frequency characteristics of multi-channel EEG signals. The EEG signals are divided into 2 second epochs and frequency spectrum up to a cutoff frequency of 45 Hz is computed. This multi-channel raw spectral data forms the input to a one-dimensional CNN (1-D CNN). Spectral data from the current, previous, and next epochs is utilized for predicting the label of the current epoch. The performance of the technique is evaluated using a dataset of EEG signals from 24 cases. The proposed method achieves an accuracy of 97.25% in detecting epileptic seizure segments. This result shows that multi-channel EEG wavelet power spectra and 1-D CNN are useful in detecting epileptic seizures.


Assuntos
Eletroencefalografia , Epilepsia , Epilepsia/diagnóstico , Humanos , Redes Neurais de Computação , Convulsões , Análise de Ondaletas
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 637-640, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018068

RESUMO

Feature extraction from ECG-derived heart rate variability signal has shown to be useful in classifying sleep apnea. In earlier works, time-domain features, frequency-domain features, and a combination of the two have been used with classifiers such as logistic regression and support vector machines. However, more recently, deep learning techniques have outperformed these conventional feature engineering and classification techniques in various applications. This work explores the use of convolutional neural networks (CNN) for detecting sleep apnea segments. CNN is an image classification technique that has shown robust performance in various signal classification applications. In this work, we use it to classify one-dimensional heart rate variability signal, thereby utilizing a one-dimensional CNN (1-D CNN). The proposed technique resizes the raw heart rate variability data to a common dimension using cubic interpolation and uses it as a direct input to the 1-D CNN, without the need for feature extraction and selection. The performance of the method is evaluated on a dataset of 70 overnight ECG recordings, with 35 recordings used for training the model and 35 for testing. The proposed method achieves an accuracy of 88.23% (AUC=0.9453) in detecting sleep apnea epochs, outperforming several baseline techniques.


Assuntos
Eletrocardiografia , Síndromes da Apneia do Sono , Frequência Cardíaca , Humanos , Redes Neurais de Computação , Síndromes da Apneia do Sono/diagnóstico , Máquina de Vetores de Suporte
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 998-1001, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018153

RESUMO

Voice command is an important interface between human and technology in healthcare, such as for hands-free control of surgical robots and in patient care technology. Voice command recognition can be cast as a speech classification task, where convolutional neural networks (CNNs) have demonstrated strong performance. CNN is originally an image classification technique and time-frequency representation of speech signals is the most commonly used image-like representation for CNNs. Various types of time-frequency representations are commonly used for this purpose. This work investigates the use of cochleagram, utilizing a gammatone filter which models the frequency selectivity of the human cochlea, as the time-frequency representation of voice commands and input for the CNN classifier. We also explore multi-view CNN as a technique for combining learning from different time-frequency representations. The proposed method is evaluated on a large dataset and shown to achieve high classification accuracy.


Assuntos
Redes Neurais de Computação , Voz , Humanos , Fala
8.
Health Informatics J ; : 1460458220951719, 2020 Aug 29.
Artigo em Inglês | MEDLINE | ID: mdl-32865113

RESUMO

To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions-such as digital scribes-must focus on identifying the 20% relevant information for automatically generating consultation summaries.

9.
J Am Med Inform Assoc ; 27(11): 1695-1704, 2020 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-32845984

RESUMO

OBJECTIVE: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. MATERIALS AND METHODS: Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. RESULTS: Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. CONCLUSIONS: AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.

10.
Sci Rep ; 10(1): 7733, 2020 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-32382048

RESUMO

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient's treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients' age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas' IDH status prediction.


Assuntos
Glioma/genética , Isocitrato Desidrogenase/genética , Prognóstico , Adulto , Idoso , Aprendizado Profundo , Feminino , Glioma/diagnóstico por imagem , Glioma/patologia , Humanos , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Mutação/genética
11.
J Med Internet Res ; 22(2): e15823, 2020 02 09.
Artigo em Inglês | MEDLINE | ID: mdl-32039810

RESUMO

BACKGROUND: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. OBJECTIVE: This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. METHODS: We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs' responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search-based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. RESULTS: The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. CONCLUSIONS: Our results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types.


Assuntos
Comunicação , Estilo de Vida , Humanos
12.
NPJ Digit Med ; 2: 114, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31799422

RESUMO

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.

13.
J Med Internet Res ; 21(11): e15360, 2019 11 07.
Artigo em Inglês | MEDLINE | ID: mdl-31697237

RESUMO

BACKGROUND: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. OBJECTIVE: The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. METHODS: We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. RESULTS: The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. CONCLUSIONS: Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.


Assuntos
Assistência à Saúde/métodos , Medicina de Precisão/métodos , Humanos
14.
Artigo em Inglês | MEDLINE | ID: mdl-28756429

RESUMO

Healthcare is currently being transformed by the introduction of genomic sequencing - a major advancement in personalised medicine. This advent provides new opportunities for clinicians to use genomic data in decision making about patient diagnosis and treatment, but this can only be achieved through access to data and support in its use. Engaging with clinicians in the development of decision support tools will optimise relevance and adoption of genomic sequencing in healthcare. In this study, existing data from clinician workshops and interviews together with horizon scanning of relevant technologies were used to define clinician portal specifications. We describe a preliminary structure of a decision support tool for use by clinicians and the manner in which the technology may be evaluated.


Assuntos
Mapeamento Cromossômico , Técnicas de Apoio para a Decisão , Genômica , Tomada de Decisões , Humanos
15.
JMIR Res Protoc ; 6(3): e32, 2017 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-28249832

RESUMO

BACKGROUND: Total knee replacement (TKR) surgeries have increased in recent years. Exercise programs and other interventions following surgery can facilitate the recovery process. With limited clinician contact time, patients with TKR have a substantial burden of self-management and limited communication with their care team, thus often fail to implement an effective rehabilitation plan. OBJECTIVE: We have developed a digital orthopedic rehabilitation platform that comprises a mobile phone app, wearable activity tracker, and clinical Web portal in order to engage patients with self-management tasks for surgical preparation and recovery, thus addressing the challenges of adherence to and completion of TKR rehabilitation. The study will determine the efficacy of the TKR platform in delivering information and assistance to patients in their preparation and recovery from TKR surgery and a Web portal for clinician care teams (ie, surgeons and physiotherapists) to remotely support and monitor patient progress. METHODS: The study will evaluate the TKR platform through a randomized controlled trial conducted at multiple sites (N=5) in a number of states in Australia with 320 patients undergoing TKR surgery; the trial will run for 13 months for each patient. Participants will be randomized to either a control group or an intervention group, both receiving usual care as provided by their hospital. The intervention group will receive the app and wearable activity tracker. Participants will be assessed at 4 different time points: 4 weeks before surgery, immediately before surgery, 12 weeks after surgery, and 52 weeks after surgery. The primary outcome measure is the Oxford Knee Score. Secondary outcome measures include quality of life (Short-Form Health Survey); depression, anxiety, and stress (Depression, Anxiety, and Stress Scales); self-motivation; self-determination; self-efficacy; and the level of satisfaction with the knee surgery and care delivery. The study will also collect quantitative usage data related to all components (app, activity tracker, and Web portal) of the TKR platform and qualitative data on the perceptions of the platform as a tool for patients, carers, and clinicians. Finally, an economic evaluation of the impact of the platform will be conducted. RESULTS: Development of the TKR platform has been completed and deployed for trial. The research protocol is approved by 2 human research ethics committees in Australia. A total of 5 hospitals in Australia (2 in New South Wales, 2 in Queensland, and 1 in South Australia) are expected to participate in the trial. CONCLUSIONS: The TKR platform is designed to provide flexibility in care delivery and increased engagement with rehabilitation services. This trial will investigate the clinical and behavioral efficacy of the app and impact of the TKR platform in terms of service satisfaction, acceptance, and economic benefits of the provision of digital services. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12616000504415; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370536 (Archived by WebCite at http://www.webcitation.org/6oKES0Gp1).

16.
Stud Health Technol Inform ; 227: 48-54, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27440288

RESUMO

Current methods to promote awareness of the sun's ultraviolet (UV) radiation have focussed on delivering population level information and some location-based reporting of UV Index (UVI). However, diseases related to excessive (e.g. sunburn, skin cancer) or insufficient (e.g. vitamin D deficiency) exposure to sunlight still remain a global burden. The emergence of wearable sensors and the application of persuasive technology in health domains raise the possibility for technology to influence awareness of sufficient sun intake for vitamin D production, as well as preventing risk of skin damage. This paper presents a personalised solution to promote healthy, safe sun exposure using wearable devices and persuasive techniques.


Assuntos
Aplicativos Móveis , Luz Solar/efeitos adversos , Raios Ultravioleta/efeitos adversos , Dispositivos Eletrônicos Vestíveis , Exposição Ambiental/análise , Exposição Ambiental/prevenção & controle , Humanos , Comunicação Persuasiva , Neoplasias Cutâneas/prevenção & controle , Smartphone , Queimadura Solar/prevenção & controle , Vitamina D/biossíntese , Vitamina D/efeitos da radiação
17.
Stud Health Technol Inform ; 214: 15-21, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26210412

RESUMO

The use of online technologies for supporting participants of behaviour change and diet program is a timely and important research direction. We present HealthierU, adaptive online portal offering a suite of interactive support tools. The portal was evaluated in a 24-week study, which shows that regular reminders trigger increased interaction with the portal. We also analyse interaction patters conducive to weight loss and discuss possible factors of the attrition rates observed in the study.


Assuntos
Informação de Saúde ao Consumidor/organização & administração , Dietoterapia/métodos , Comportamentos Relacionados com a Saúde , Promoção da Saúde/organização & administração , Internet/organização & administração , Programas de Redução de Peso/organização & administração , Austrália , Sistemas de Informação em Saúde/organização & administração , Serviços de Saúde do Trabalhador/organização & administração , Sistemas de Alerta , Comportamento de Redução do Risco
18.
J Telemed Telecare ; 19(3): 166-174, 2013 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-23520213

RESUMO

We developed and tested a mobile phone application (app) to support individuals embarking on a partial meal replacement programme (MRP). Overweight or obese women were randomly allocated to one of two study groups. The intervention group received an MRP Support app. The control group received a static app based on the information available with the MRP. A total of 58 adult women (Support n = 28; Control n = 30) participated in the 8-week trial. Their BMI was 26-43 kg/m2 Usage data suggested that the intervention group were more engaged with using the app throughout the study period. Mixed modelling revealed that the difference in weight loss between the intervention and control groups (estimated mean, EM = 3.2% and 2.2% respectively) was not significant (P = 0.08). Objective data suggested that users of the Support app were more engaged than those using the control app. A total of 1098 prompts (54%) asking people in the intervention group to enter their meals were completed prior to the evening prompt. Women in the intervention group reported a greater increase in positive affect (i.e. mood) than those in the control group (EM = 0.48 and -0.01, respectively) (P = 0.012). At Week 8, those in the control group reported a greater decrease in the effort they were willing to put into staying on the diet than those who received the Support app (EM = -2.8 and -1.4, respectively) (P = 0.024). The Support app could be a useful adjunct to existing MRPs for psychological outcomes.


Assuntos
Telefone Celular , Aplicativos Móveis , Obesidade/dietoterapia , Programas de Redução de Peso/métodos , Adulto , Dieta Redutora/métodos , Retroalimentação , Feminino , Humanos , Pessoa de Meia-Idade , Sobrepeso/dietoterapia , Projetos Piloto , Apoio Social , Adulto Jovem
19.
J Med Internet Res ; 14(6): e173, 2012 Dec 12.
Artigo em Inglês | MEDLINE | ID: mdl-23234759

RESUMO

BACKGROUND: Obesity remains a serious issue in many countries. Web-based programs offer good potential for delivery of weight loss programs. Yet, many Internet-delivered weight loss studies include support from medical or nutritional experts, and relatively little is known about purely web-based weight loss programs. OBJECTIVE: To determine whether supportive features and personalization in a 12-week web-based lifestyle intervention with no in-person professional contact affect retention and weight loss. METHODS: We assessed the effect of different features of a web-based weight loss intervention using a 12-week repeated-measures randomized parallel design. We developed 7 sites representing 3 functional groups. A national mass media promotion was used to attract overweight/obese Australian adults (based on body mass index [BMI] calculated from self-reported heights and weights). Eligible respondents (n = 8112) were randomly allocated to one of 3 functional groups: information-based (n = 183), supportive (n = 3994), or personalized-supportive (n = 3935). Both supportive sites included tools, such as a weight tracker, meal planner, and social networking platform. The personalized-supportive site included a meal planner that offered recommendations that were personalized using an algorithm based on a user's preferences for certain foods. Dietary and activity information were constant across sites, based on an existing and tested 12-week weight loss program (the Total Wellbeing Diet). Before and/or after the intervention, participants completed demographic (including self-reported weight), behavioral, and evaluation questionnaires online. Usage of the website and features was objectively recorded. All screening and data collection procedures were performed online with no face-to-face contact. RESULTS: Across all 3 groups, attrition was high at around 40% in the first week and 20% of the remaining participants each week. Retention was higher for the supportive sites compared to the information-based site only at week 12 (P = .01). The average number of days that each site was used varied significantly (P = .02) and was higher for the supportive site at 5.96 (SD 11.36) and personalized-supportive site at 5.50 (SD 10.35), relative to the information-based site at 3.43 (SD 4.28). In total, 435 participants provided a valid final weight at the 12-week follow-up. Intention-to-treat analyses (using multiple imputations) revealed that there were no statistically significant differences in weight loss between sites (P = .42). On average, participants lost 2.76% (SE 0.32%) of their initial body weight, with 23.7% (SE 3.7%) losing 5% or more of their initial weight. Within supportive conditions, the level of use of the online weight tracker was predictive of weight loss (model estimate = 0.34, P < .001). Age (model estimate = 0.04, P < .001) and initial BMI (model estimate = -0.03, P < .002) were associated with frequency of use of the weight tracker. CONCLUSIONS: Relative to a static control, inclusion of social networking features and personalized meal planning recommendations in a web-based weight loss program did not demonstrate additive effects for user weight loss or retention. These features did, however, increase the average number of days that a user engaged with the system. For users of the supportive websites, greater use of the weight tracker tool was associated with greater weight loss.


Assuntos
Internet , Obesidade/terapia , Sobrepeso/terapia , Perda de Peso , Adulto , Austrália , Exercício Físico , Humanos , Apoio Social
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA