Ms 52 Solved Assignment 2012

The motion on a trampoline can be described by a combination of free fall and harmonic motion: While in the air, the trampolinist is only affected by the force of gravity, and during the contact time the trampolinist also experiences a large upward force from the trampoline bed. The stronger the push from the trampoline, the larger the acceleration and the higher the jump. In earlier work [1, 2], the motion on a trampoline was analysed in detail, showing the relation between the maximum force on the rider and the ratio between 'flight time' and contact time. The mathematical description was found to be in good agreement with measurement.

In this paper, we present an assignment based on Rosannagh MacLennan's gold medal trampoline routine from the London olympics in 2012. She used 19 s (as extracted from the video, [3]) to complete the routine of 10 jumps. The score board shows that 16 of these seconds were 'flight time' [3]. Several groups of students were asked to draw approximate graphs of elevation, velocity and acceleration during two full jumps of her routine (with the approximation that all jumps are similar). To help the respondents, an empty graph with suitable axes was provided. In preparing this assignment, we hoped that earlier kinaesthetic experiences of trampoline bouncing might help students recognise the strong forces during the acceleration at the bottom of the jump.

The assignment requires students to distinguish between the two different types of motion and the forces involved. They also need to work out a strategy for applying familiar relations for height, velocity and acceleration, as well as using the relations between these different aspects of motion. How do students deal with this assignment? What aspects are most challenging?

This trampoline example was first used in 2014 as part of the Swedish national competition for the European union science olympiad (EUSO) [4], with a few scaffolding questions, presented below. The following year, a multiple-choice version of the question was used, as discussed in section 4. Different versions of the problem have then been used for group discussions during workshops for physics teachers but also as an exam problem for first-year university physics students, as discussed in section 5.

Our goal with this investigation is to outline and map students' understanding of representations of these kinematics concepts as a support for the development of teaching strategies.

1.1. Understanding of graphs of position, velocity, and acceleration versus time

Graphs of position, velocity and acceleration are fundamental in teaching about motion, and many researchers have studied student difficulties, as reviewed e.g. in [5]. Even if students entering introductory physics classes understand the basic construction of graphs, they often find it difficult to apply those skills to the tasks they encounter in the physics courses [6]. These difficulties often arise due to lack in using multiple representations in problem solving [7], but also due to limitations in reading representation such as graphs [8]. Student limitations in discerning critical aspects, are often found to hinder students from being able to understand a problem on a deeper level [9]. Students simply do not see the same things in those representations as their teachers do, while at the same time teachers have forgotten how difficult it was to read representations being a novice. However, proper scaffolding by experienced teachers is found to improve student representational competency and hence increase the success by the students [10]. Meltzer [11] compared students' problem solving performance depending on representations. They found that the proportion of correct responses was consistently higher for questions posed verbally than for analogous questions posed in a diagrammatic representation.

McDermott et al [6] and Beichner [12] noted that students see little difference between distance, velocity, and acceleration, and often believe that graphs of these variables should look identical: if students believe that a 'graph is like a picture', it should not matter what is graphed, and they may expect all graphs to look like a replication of the object's physical motion. McDermott et al also found that, although there was differences in severity, the types of difficulties were similar for high-school students, and for physics and physical sciences college students.

Planinic et al [13, 14] compared student responses to analogous problems in mathematics and physics. They also asked physics teachers to rank the difficulties of the tasks. While most physics teachers were found to rank the context-free mathematics problems as more difficult, students were considerably more likely to give correct responses for the context-free versions of the problems, supporting the suggestion of McDermott et al [6] that the lack of mathematical skills is not the main cause of student difficulties with graphs in physics. Training students to use multiple representations is known to be important, as emphasized e.g. by van Heuvelen [7].

Acceleration is typically introduced without reference to force, as seen e.g. in Basson's analysis [15] of the 'hierarchy of physics concepts to deal with the concept acceleration'. In the trampoline example, we can make use of the personal experience of the varying forces acting on your own body as you bounce. This kinaesthetic experience can be enhanced by accelerometer data [1, 2] e.g. from a smartphone [16], as well as a slinky for a visual measure of the force [17], and video analysis of the motion [18] to provide a wider variety of representations.

To analyse Rosannagh MacLennan's gold medal trampoline jumps, a few scaffolding questions can be used, e.g.

  • (i)  

    What time is required to reach the highest point after the feet have left the trampoline?

  • (ii)  

    Draw a rough graph of how her height varies during two full jumps.

  • (iii)  

    Draw a graph of the variation of velocity during two jumps.

  • (iv)  

    What is her velocity as she lands on the trampoline?

  • (v)  

    What is her velocity as she leaves the trampoline again?

  • (vi)  

    In what positions is her speed zero?

  • (vii)  

    What is her acceleration when she does not touch the trampoline?

  • (viii)  

    Estimate her acceleration during the time her feet are touching the trampoline.

These questions were included as the problem was first used to select the 24 finalists for the Swedish national competition for the European union science olympiad (EUSO) in 2014. The Swedish competition was open for students in the last year of lower secondary school and in the first year of upper secondary school, (15 and 16- years old, respectively in the autumn of the school year), with approximately 12 finalists selected from each age group. The first tests were graded by the local teachers, who then sent up to three tests from each school for final grading. The response sheets from 18 schools with potential finalists were analysed, giving a total of 42 response sheets. Of the 22 (20) responses from 15(16) -year-olds, only 11(8) had graphs. All elevation graphs were essentially correct, whereas most of the velocity graphs indicated confusion between velocity and speed.

The scaffolding questions above have also been used to initiate small-group discussions during teacher workshops. During these occasions, we have found that teachers typically first conclude that each jump has a flight time of around s and thus reaches the highest point after  s. From this information, the maximum elevation is obtained using the well-known relation  m—often after some discussion to clarify that the same time  s is used to reach the trampoline after passing the highest point. Other groups may instead start by identifying points where the velocity is zero (i.e. at the highest and lowest points). The initial strategies also include drawing the acceleration () for the part of the motion when the trampolinist is in the air. Figure 1 shows an example of how these values related to the free-free fall parts of the jumps can be inserted in the diagram, before attempting an estimate of the acceleration during contact time.

In the next section we show redrawn versions of a number of common responses from students challenged with this task, and describe briefly the features of the different types of responses.

The types of responses shown in this section have been found in most student groups, although the university students were more likely to draw the later examples. The responses have been grouped into a number of categories, and have been redrawn for the presentations in this section. These examples may be used for group discussions as a way to elicit and clarify thoughts about the relation beetween elevation, velocity and acceleration. Selected velocity graphs have also been used for a multiple-choice question, as discussed in the next section.

3.1. The same graphs for the same motion

In the first set of graphs, shown in figure 2, the respondents have correctly assigned the value zero at the turning points. However, as in many other types of responses the velocity is never drawn to be negative—the students seem to forget that velocity is a vector and that it continues below zero as the jumper is on the way down after the highest point. From responses handed in on paper, it is often obvious that students have tried to avoid discontinuous derivatives, adjusting their graphs to making the velocity change smoothly at the turning points and changing their original graph to something like a sine function. Many of these respondents drawing that type of velocity graph make the acceleration graph very similar, as in figure 2. This set of graphs also illustrates the tendency, found by McDermott et al (1987) and Beichner (1994) of students to make graphs representing the same motion to look similar. Other responses, indeed, showed the velocity and acceleration graphs essentially identical also to the elevation graph.

We also note that in the graphs in figure 2 the acceleration is drawn as zero in the same points as the velocity, which is known to be common problem e.g. from conceptual questions asking about the acceleration of a ball in the highest point.

Some of the responses similar to figure 2 have omitted the short time, s, when the trampolinist is in contact with the trampoline, or have forgotten that the elevation is negative during the short contact time (choosing the level of the unloaded trampoline mat as zero). The omission was found also for the responses that were otherwise similar to those in figure 3.

3.2. Positive and negative acceleration during free fall

The velocity in the graph in figure 3 is always positive and has a slope while the trampolinist is on the way up and a slope on the way down, indicating a common confusion between velocity and speed. In this case, the acceleration is still drawn as for the whole free-fall part of the motion. For the somewhat easier question about acceleration and velocity for a ball thrown into the air, students often draw the acceleration changing sign at the highest point. Some textbooks emphasize the difference between speeding up and slowing down, and refer to the slowing down as 'negative acceleration', which may encourage that type of velocity graphs, (as well as the common incorrect belief that the acceleration must be zero at the turning point).

The relation between the integral of velocity and the change in elevation does not hold for the graphs in figure 3. Nor do the acceleration graph during contact time correspond to the derivative of velocity.

It should be noted that it is more common for students to draw a constant acceleration during the contact time, typically at  ±, but sometimes at larger values, with an area under the positive part of the acceleration graph, giving the same magnitude as the integrals of the negative part. Of course, neither version is consistent with the velocity graph in figure 3.)

3.3. Uniform acceleration

As indicated in figure 1 the behaviour during contact time requires additional information or approximations. What is known is the velocity at the beginning and end of the contact time, and that the integral of the acceleration during that time must lead to this change. A reasonable, albeit nonrealistic, approximation is to use constant acceleration during the contact time, as done in the set of graphs in figure 4, where elevation, velocity and acceleration graphs are consistent. A version of this graph drawn by many students is to simply change the sign of the acceleration for the contact time (giving ), which, however, leads to a broken relation between the integrals of positive and negative acceleration.

The velocity graph in figure 4 looks very similar to the graphs when Hooke's law is used to describe the force during the contact time [1], which results in the graphs in figure 5. The main difference is that the acceleration is described by a continuous function (even if the derivative is discontinuous [2]), and that the maximum acceleration is larger.

The trampoline assignment was converted to multiple-choice version used in the 2015 competition, as part of the written test used to select 24 students to take part in the finals. Typical velocity graph responses were drawn (figure 6) and students were asked to mark which one would best describe how velocity varied with time during the jumps. The competitors were asked to explain their choices, after writing down what forces act during the time in the air and in contact with the mat.

The graphs in figure 6 were based on the responses in the 2014 competition, as well as on discussions with first-year university students who were asked to draw elevation, velocity and acceleration for a ball thrown up into the air.

The graphs in figure 6 have also been used for initial group discussions during teacher workshops. The dotted 'zero' lines were not included in the graphs used for the competition, but were added after a few teachers in a workshop suggested that it would make the assignment easier.

Below, we summarize the responses to this question from the 24 students, who made it to the Swedish final. Two of the finalists made no choice of graph.

Graph A was chosen by seven of the students. All their motivations were along the line 'The velocity has a minimum both at the highest and lowest point' or 'The velocity should be zero twice per bounce'.

Three students (all from lower secondary school) chose alternative C, noting e.g. that 'the velocity first decreases and then increases equally fast, since the force of gravity is constant'.

Two students (both from upper secondary school) chose alternative D, noting the strong acceleration in contact with the mat. Their comments indicate that they discuss speed rather than velocity.

None of the finalists had chosen alternative E, which could be seen as a first approximation to the correct graph B. One of the four students choosing alternative F excluded the graph in E (and also C and D), due to the sharp changes, and rejected B for lack of symmetry. The motivations for graph F describe the motion, fast on the way up, stopping at the top, moving down faster and faster until you hit the mat—without noting that the graph does not correspond to their description of the velocity.

Five students chose the correct response, B, noting e.g. that 'The acceleration is constant, except when you are in contact with the trampoline', one of the students also adding a comment about the acceleration being large during the contact time. One student first chose D, due to the number of zero-velocity instances, and then crossed over that answer, adding comments 'If it is velocity, rather than speed, B is correct' and 'The acceleration is not constant during the contact time, as it is shown in D'.

In October 2016, the trampoline problem was used as one of six problems for a five-hour written exam for the first-semester physics students at Lund university. The initial scaffolding questions

  • (i)  

    What forces act on the trampolinist while she is in the air and when she is at the lowest point?

  • (ii)  

    What time is required to reach the highest point after the feet have left the trampoline?

were followed by an instruction to draw approximative graphs over elevation, velocity and acceleration during two full jumps, with an empty version of figure 1 provided. In addition, students were asked to estimate the maximum force from the trampoline acting on her, using m for her mass.

The initial scaffolding question about forces acting on the trampolinist seems to have steered most students away from the common incorrect assignment of zero acceleration at the highest point.

To get full score, the free fall parts of the diagrams had to be correct and the integrals over positive acceleration must compensate the longer free-fall periods of negative acceleration. Of the 93 students taking the exam, only 12 scored the maximum 5 points on the problem. No deduction was made for using the approximation of constant acceleration during contact time, but four students discussed going beyond that approximation. Eight students had 1 point deducted, subtracting mg rather than adding mg to get the force (or forgetting mg), or using incorrect scales on the axes. Of the ten students receiving 3 points, several had drawn a positive acceleration of the same magnitude as the acceleration of gravity, drawn speed rather than velocity, or failed in other ways to use the relations between elevation, velocity and acceleration. Many of the students receiving 3 points or less had not drawn any graph, but only provided numerical answers.

The results in this work confirm earlier research showing that students do not necessarily make the connection between kinematics concepts and the corresponding mathematical description. Even if students draw a reasonably correct velocity diagram, the large slope during the contact time is often not reflected in a large value for the acceleration, even for first-semester university students. With a too small value for the upward acceleration, the area under the graph is insufficient to compensate for the downward acceleration during the longer free fall part.

Difficulties in interpreting integrals have been found in other contexts. E.g. Nguyen and Rebello [19] found that 'only a few students could recognize that the concept of area under the curve was applicable in physics problems' and Planinic et al [14] found that students have much less difficulty with the concept of graph slope than for the concept of area under the graph.

Connecting a conceptual understanding with the mathematical description should be an important aspects of physics teaching. Indeed, first-year undergraduates who were taught about integrals with a focus on conceptual understanding have been found to score 'significantly higher than the students in procedural-based environment on assessment that measures conceptual understanding as well as procedural skills' [20]. Similar results were found by Kohl and Finkelstein [21]. That students are often able to solve difficult end-of-chapter problems without managing apparently simpler conceptual questions is well documented by physics education research (see e.g. [22]) and keeps being rediscovered by new generations of physics teachers.

Woolnough [23] suggested that senior secondary students operate in 'three distinct contexts: the real world, the physics world, and the mathematical world, each with different characteristics and belief systems' and found that students resisted applying their mathematical knowledge to physics. Many everyday situations are too complicated for simple mathematics or physics descriptions and often need so many approximations that the mathematics and physics may no longer seem relevant. However, the easy access to electronic data taking using students' own smartphones [16] makes it easy to obtain graphs describing familiar events.

The analysis of trampoline jump offers the possibility to discuss many common problems concerning the understanding of position, velocity and acceleration. Students' personal experiences of trampolining can be used to bridge mathematical modelling to standard physics texbook cases, as well as to authentic measurement data. We hope that the examples in this paper illustrate the rich opportunities offered by the trampoline assignment and would like to invite others use the problem and to collect and share data on responses for different student groups.

We would like to express our appreciation to Urban Eriksson for helpful discussions.

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Abstract

Purpose: Telehealth care is emerging as a viable intervention model to treat complex chronic conditions, such as heart failure (HF) and chronic obstructive pulmonary disease (COPD), and to engage older adults in self-care disease management. Design and Methods: We report on a randomized controlled trial examining the impact of a multifaceted telehealth intervention on health, mental health, and service utilization outcomes among homebound medically ill older adults diagnosed with HF or COPD. Random effects regression modeling was used, and we hypothesized that older adults in the telehealth intervention (n = 51) would receive significantly better quality of care resulting in improved scores in health-related quality of life, mental health, and satisfaction with care at 3 months follow-up as compared with controls (n = 51) and service utilization outcomes at 12 months follow-up. Results: At follow-up, the telehealth intervention group reported greater increases in general health and social functioning, and improved in depression symptom scores as compared with usual care plus education group. The control group had significantly more visits to the emergency department than the telehealth group. There was an observed trend toward fewer hospital days for telehealth participants, but it did not reach significance at 12 months. Implications: Telehealth may be an efficient and effective method of systematically delivering integrated care in the home health sector. The use of telehealth technology may benefit homebound older adults who have difficulty accessing care due to disability, transportation, or isolation.

Telehealth, Heart disease, Home care, Homebound

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) frequently coexist, share similar clinical presentations among older adults, and require multifaceted treatment procedures (Hawkins, Petrie, Jhund, Chalmers, Dunn, & McMurray, 2009; Mascarenhas, Azevedo, & Bettencourt, 2010). Heart failure is a progressive and disabling medical condition estimated to affect 5.7 million Americans and consumes an estimated total of $27 billion dollars in annual treatment expenditures (Rosamond et al., 2008). HF is a leading cause of cardiovascular-related morbidity and mortality, and it is not uncommon to see the combination of HF with COPD in advanced illness among older adults (Rutten, Cramer, & Lammers, 2006). COPD is a progressive and irreversible condition in which the airways of the lungs are damaged and unable to process oxygen well. HF and COPD can influence each other, cause the same key symptom—shortness of breath, and are frequently treated simultaneously (Starling, 2009).

We report on a randomized controlled trial to test the impact of the Telehealth for Heart Education Activation Rehabilitation and Treatment (tele-HEART) intervention on health and mental health outcomes of homebound medically ill older adults diagnosed with HF or COPD receiving home health skilled nursing care. This study applied the concept of telehealth therapy technology to the medical care of these frail patients.

Home health care patients represent a diverse, isolated, medically frail, and high medical cost group of homebound older adults who frequently have financial stressors, numerous medical conditions, and limited access to care (U.S. Department of Health and Human Services, 2002). Generally, home health care is limited up to 90 days of care for an individual patient. In the home health care sector, one strategy to reduce costs for agencies and barriers in accessing health services for older patients (e.g., disabled, isolated, transportation) may be to utilize telehealth care technology. Telehealth is defined by the Health Research and Services Administration (2011) as “the use of electronic information and telecommunications technologies to support long distance clinical health care, patient and professional health-related education, public health and health administration.” Telehealth innovations have been instrumental in conducting patient assessment, health and psychological interventions, patient and provider education, and remote patient monitoring on a real-time basis with critical information feedback between patient and provider.

The tele-HEART program, a multidimensional telehealth treatment model in the home health care setting was developed to improve the quality of heart health care for frail homebound older adults. The tele-HEART model builds on lessons learned from prior studies to improve health care services for older adults diagnosed with heart failure via telehealthcare initiatives (Barlow, Singh, Bayer, & Curry, 2007; Clark, Inglis, McAlister, Cleland, & Stewart, 2007; Kobb, Chumbler, Brennan, & Rabinowitz, 2008). Prior reviews of the literature suggest targeting specific subpopulations of individuals with chronic illness, incorporating meaningful comparison conditions, and examining the factors that enhance the quality of the care and its relationship with treatment outcome (Bensink, Halley, & Wootton, 2008; Ditewig, Blok, Havers, & van Veenendaal, 2010; Van der Kooy, van Hout, Marwijk, Marten, & Stehouwer, 2007). Researchers acknowledge the significance of patient self-care in their overall health care management (Polisena et al., 2010; Radhakrishnan & Jacelon, 2011). Building on the telehealth care delivery model, we designed the tele-HEART intervention to improve the provision of geriatric home health services and specifically increase the likelihood that older adults receive recommended chronic disease care management protocols and participate in their own care.

Unique features of the tele-HEART intervention compared with prior studies of home health care interventions include the following: (a) in-home assessment; (b) in-home setup and education on the telehealth monitoring device; (c) ongoing care management provided by a telehealth home care nurse specialist; (d) use of home care protocols for evaluation and management of HF and related COPD and comorbid depression; (e) utilization of an integrated electronic medical record; (f) use of a computerized care management tracking tool; and (g) our prior research on integrated mental health service delivery by home health care teams.

We hypothesized that compared with a usual care plus psychoeducation control, patients enrolled in the tele-HEART intervention would receive significantly better quality of care for HF/COPD using chronic disease management protocols resulting in improved health-related quality of life, mental health status, and satisfaction with care at 3 months follow-up and decreased emergency room use and hospital admissions at 12 months follow-up.

Design and Methods

Setting

The IRB approved randomized controlled study was conducted at the “university affiliated” one of the largest hospital-based Medicare-certified home care programs in “State”. The agency is JCAHO-accredited and offers home-based health services to a diverse group of older adults including African American (13%), Latino (4%), Asian (1%), and Caucasian (82%) living in a large four-county region.

Recruitment

Prospective participants, 65 years or older, were recruited from hospital discharge planners local physicians, cardiologists, cardiothoracic surgeons, and administrators of community health centers who were notified of the study by the “university affiliated” marketing director and were instructed about the characteristics and benefits of the new telehealth care program.

Individuals who were admitted for services to the home health care agency were routinely screened and informed of the study by a nurse research coordinator trained by the first author. Eligible patients who expressed interest were provided further details about the project. Specifically, prospective participants were informed of a new home-based telehealth monitoring program for older patients with a diagnosis of HF or COPD. Patients were contacted and screened within 3–5 days after admission to home care or after receiving a referral from a discharge planner, physician, or case manager and were invited to participate in the tele-HEART treatment program. Patients were enrolled in the study if they met the following inclusion criteria: (a) 65 years or older and a medical chart diagnosis of Heart Failure or Chronic Obstructive Pulmonary Disease; (b) patient experienced frequent health care encounters (i.e., hospitalized twice in the last 6 months or seen at least twice in the Emergency Room in the past 2 months); (c) patient required three or more home visits per week; (d) patient consent to participate in the program with random assignment; and (e) patient was willing to learn how to use the telehealth monitoring system. Older patients that were excluded from the study were (a) unable to learn and use the HomMED telehealth device due to physical disability; (b) cognitively impaired based on medical chart diagnosis and had no caregiver; (c) exhibited behavioral/problems (e.g., aggression, agitation, delirium, paranoia) that interfered with learning how to use the HomMED telehealth device and communicating with the telehealh nurse.

Experimental Design

The project coordinator contacted interested participants who met inclusion criteria to obtain their informed consent. They were informed that participation would involve random assignment to telehealth intervention or usual care plus education services, the possible use of a telehealth monitoring unit in the home if assigned to the experimental condition, periodic communication with a research assistant over a 12-month period, and completion of study questionnaires at baseline and approximately 3 months postbaseline. Once consent was obtained by the project coordinator, an appointment was made by a research assistant to conduct baseline assessment prior to assignment to condition.

Telehealth Monitoring Unit

This study used the Honeywell “HomMed” Health Monitoring System (HomMed, 2011), which consists of a small, tabletop in-home monitor and a Central Station located at the home health care agency. The data collection process took approximately 10 min in the home, required only a few key presses, and utilized voice and text commands in a variety of languages and volume levels. The monitor offered easy readability with a 20-character, two-line, vacuum fluorescent display and large font. The daily monitoring of weight, noninvasive blood pressure, pulse, oxygen saturation, and temperature had a preset scheduled time set for each patient based on patient preference. Audio prompts instructed patients through each step of the monitoring process and short text prompts remained on the monitor face to cue patients until the task was completed. A selection of modifiable questions was customized to the client’s condition, requiring a Yes/No answer. The patient’s response was captured with a single key press, and instructions were written for a sixth-grade level and were supported with graphics.

The telehealth monitoring system transmitted patient data via a telephone line from the home monitoring unit located in the patient’s home to a central station located at the home care office. All transmitted data were encoded and stripped of patient-identifying information. The transmitted data were not associated with a patient until it was successfully received by the Central Station. Upon receipt, the central station checked the data to ensure it had retained integrity and then stored it in an encrypted format within a protected computer database. Patient data was displayed and triaged by color coding to allow immediate determination of nurse plans of care, tasks, and counseling. Patients having abnormal readings were contacted by the telehealth nurse for further evaluation. The clinician could quickly and easily email, print, or fax trend data to any member of the home care team. Additional reports that can be included as documentation in the electronic medical record were patient note history; vitals compliance history (e.g., blood pressure, weight, pulse, temperature, etc.); patient demographics; and listing of physicians.

Random Assignment

A simple randomization strategy (with a random numbers table) was used by the first author to assign participants to the usual care plus education control group or to the tele-HEART intervention group after informed consent was obtained.

Interventionist Training

Two registered homecare nurses and a licensed practical nurse acted as telehealth therapy providers during the study. They were supervised by the homecare telehealth nurse manager who acted as project coordinator. The telehealth nurses received a week of training on the installation and use of the equipment by a Honeywell technical representative. Training materials included a user guide and patient teaching sheets and topics included use of monitors and peripheral devices, care and cleaning of the devices, and a patient log for tracking vital signs. The nurses were also trained in psychoeducation and problem-solving therapy strategies, based on our previous research (Gellis, 2010; Gellis & Bruce, 2010; ) using an empirically tested and established treatment manual (Gellis, McGinty, Horowitz, Bruce, & Misener, 2007) on integrated delivery of health and mental health services in home care settings to address the daily stressors older patients face while managing a chronic condition.

Telehealth Care Intervention

During the initial in-home visit, the telehealth nurse set up the monitoring system and provided a detailed 1-hr tutorial session on its use. As part of the practice protocol, the nurse required a feedback demonstration of the equipment by the patient as evidence of knowledge and skill acquisition. Patients were informed of the normal clinical parameters and instructed as to when they should contact the telehealth nurse. The telehealth monitoring system was provided to home care patients to enhance the patients’ self-management of their medical condition through a greater understanding of their disease processes. Patients obtained education on the disease process and counseling about the importance of daily monitoring of body weight, smoking cessation, behavioral activation, proper diet, medication adherence, problem solving strategies on managing their daily medical condition, and monitoring of symptoms that may be indicative of worsening heart failure. Counseling was tailored to each patient’s medical and psychological needs. The telehealth nurse was available to the patient daily, by telephone, and also for urgent home visits as needed.

On a daily basis, the telehealth nurse would review patient data, which included vital signs, heart rate, weight, blood pressure, pulse, oxygen saturation, and temperature, that were sent over the internet via the HomMED communication device. A preset scheduled time for collection of vital signs was arranged with each patient based on their preference. Audio prompts instructed patients through each step of the health data collection process. Short text prompts were also on the HomMED monitor display to cue patients until the requested task was completed. Sufficient time was given for patients to successfully perform each task. Telehealth nurses contacted patients based on abnormal findings from daily-transmitted clinical data. The most common reasons for contacting patients included (a) weight gain, (b) blood pressure, and (c) shortness of breath. An ongoing collaborative process was established between the telehealth nurse and the older patient. Another significant component of the telehealth intervention included a selection of modifiable (YES/NO) questions that was customized to the patient’s condition. Patients had increased access and convenience to the telehealth nurse. Patients were informed that they could contact the telehealth nurse on a daily basis if needed. This process also offered older patients a daily structure and routine to learn more about and manage their medical conditions.

Usual Care

Participants assigned to the usual care plus education control group received standard home care services provided by registered nurses in the role of case managers. The nurses developed and managed the patient plan of care, which may have included one or more of the following services: physical therapy, social services, nutrition, and home aides based on patient needs and the plan of care. After the initial intake for registration, face-to-face home visits were conducted weekly to provide treatment based on patient goals and treatment progress over a 90-day period. Health education was provided to the patient on heart and/or chronic respiratory disease during home visits.

Measures

Participants completed study assessments at baseline within 1 week prior to the assignment to condition and again at postintervention (3 months) by telephone interviews conducted by a research assistant who was blinded to the participant’s randomization status. We collected the prior 12-month hospital service utilization data (emergency room use and hospitalization days) of enrolled patients and again at 12-month postbaseline.

Depressive Symptoms.—

Depression was measured using two instruments: the Center for Epidemiologic Studies Depression (CES-D) Scale and the Patient Health Questionnaire (PHQ). The CES-D (Radloff, 1977), an 11-item self-report modified from the original scale was used to measure depressive affect, somatic symptoms, positive affect, and interpersonal relations. This version was originally used in the Established Populations for Epidemiologic Studies for the Elderly (EPESE) study (Kohout, 1992). The 11-item scale contained a three response format (0 = hardly ever or never; 1 = some of the time; 2 = much or most of the time) for each item with an established cut-point of 16 or greater indicating depression. This measure was used because it is administered at intake by the homecare nurse as part of the agency’s routine care. However, it is only a screen for depression and thus requires further assessment if the screen is positive.

The PHQ-9 (Kroenke & Spitzer, 2002), a well-established depression module of the full PHQ (PRIME-MD) diagnostic instrument, was also used to assess depressive symptoms because it provides a provisional diagnosis and a severity score in order to select, guide, and monitor treatment. The PHQ-9 consists of nine questions about how often the respondent has been bothered by depressive symptoms during the past two weeks. Scores for each item range from 0 (not at all) to 3 (nearly every day). Total scores of 0–4 indicate no depression, 5–9 indicate mild depression, 10–14 indicate moderate depression, 15–19 indicate moderately severe depression, and 20–27 indicate severe depression. In this study, a score of 10 or greater was evidence of clinically significant depression requiring further evaluation.

Medical Outcomes SF-36.—

We assessed health-related quality of life using three of the eight validated and well-established scales of the Medical Outcomes Study Short Form Health Survey (SF-36; Ware, Kosinski, & Keller, 1994): (a) General Health, (b) Bodily Pain, and (c) Social Functioning.

Patient Satisfaction Survey.—

A survey developed by the home care agency assessed satisfaction with services. Six questions included the patients’ satisfaction with the telehealth experience, problems using the equipment, concerns about privacy when using the equipment, whether the telehealth intervention helped to improve their overall health, helped them stay healthier, and improved their understanding of their illness. Patients were asked to rate their care, using a Like scale from 1 to10, with “10” being the highest satisfaction rating, and scores range from 6 to 60. The usual care patients completed the identical survey answering questions substituting the word “telehealth” with “usual homecare services.”

Statistical Analysis

We compared baseline demographic characteristics using independent sample t tests and chi-square tests. We conducted an “intent to treat” (ITT) analysis with all randomized patients kept in the analysis. Multilevel modeling also known as hierarchical linear models, mixed effects models, random coefficient models, and multilevel models is an effective option for performing ITT analyses. Random effects regression models (RERMs) was the main analytic method used for assessing outcome measures and intervention robustness on the change between baseline and follow-up measurements. Outcome measures were analyzed at baseline and again at posttest by using RERMs in order to test for the effects of condition, time, and condition by time interaction. Since we hypothesized significant changes over time in participant outcomes, we deemed the condition by time interaction effects to be most relevant rather than time or main effects. RERMs offer several advantages over other repeated measures designs because they include missing case data (Hedeker & Gibbons, 2006). The final step included an assessment of participant self-reports of satisfaction with the treatment in both groups. A t test compared patient satisfaction questionnaire scores at posttreatment only in both groups.

Results

Sample Characteristics

During enrollment, a total of 214 patients were assessed for eligibility. Of those, 73 did not meet inclusion criteria and 26 declined to participate (12% refusal rate). A total of 115 patients were enrolled and randomized: 57 patients were randomized to the treatment group and 58 were randomized to the control group (Figure 1). As reported in Table 1, comparative analyses at pretreatment of baseline demographics and health-related participant variables showed no significant differences among groups. More than 60% of the sample was female with a mean age of 79 years, whereas 22.5% were aged 85 or older. A majority were widowed and were also Medicare recipients (85%), and 25% had incomes less than $14,000 per year. Approximately 44% of participants lived alone. A majority (60%) had a prognosis of poor to fair, and all patients were home health care recipients with a main diagnosis of either HF (81%) or COPD (19%). Patients had a mean of 3.95 other concurrent diagnoses and 18% had a history of substance abuse. More than a third had some type of hearing impairment. The majority of intervention and control group participants were independent in basic activities of daily living (ADLs; 66% vs. 70%; p = .38). Overall, patients had a mean of 3.3 ADL impairments, with the most frequent being dressing, bathing, and ambulating. About 53% of patients resided with a family caregiver.

Table 1.

Baseline Characteristics of Homebound Patients by Study Groupa

Characteristic Tele-HEART (n = 57) Control group (n = 58) 
Age, M (SD) years 80.1 (7.8) 78.3 (6.9) 
Marital status, % married 37.3 31.4 
Education, % <12 years 51.2 53.1 
Female, % 62.7 68.6 
Income, < $14,000, % 29.6 30.1 
Lives with caregiver, % 55.3 60.8 
Medicaid recipient, % 17.6 21.6 
Vision impairment, % 12.9 11.8 
Hearing impairment, % 37.3 38.2 
Disease group, % 
    HF 80.7 74.1 
    COPD 19.2 25.8 
Comorbid medical  conditions, % 
     Hypertension 46.1 47.3 
     Diabetes mellitus 24.3 23.9 
     Osteoarthritis 21.7 22.4 
     Cancer 3.5 4.1 
MMSE, M (SD) score 25.1 (1.7) 25.8 (0.6) 
ADLs, M (SD3.5 (1.6) 3.3 (1.8) 
Health services utilization 
 ER visits in past twelve   months, M (SD0.8 (0.8) 0.7 (9.3) 
 Hospital days in past   12 months, M (SD13.9 (9.9) 14.3 (9.7) 
 Patient episodes of   homecare service in past   twelve months, M (SD1.9 (1.2) 2.0 (1.2) 
Characteristic Tele-HEART (n = 57) Control group (n = 58) 
Age, M (SD) years 80.1 (7.8) 78.3 (6.9) 
Marital status, % married 37.3 31.4 
Education, % <12 years 51.2 53.1 
Female, % 62.7 68.6 
Income, < $14,000, % 29.6 30.1 
Lives with caregiver, % 55.3 60.8 
Medicaid recipient, % 17.6 21.6 
Vision impairment, % 12.9 11.8 
Hearing impairment, % 37.3 38.2 
Disease group, % 
    HF 80.7 74.1 
    COPD 19.2 25.8 
Comorbid medical  conditions, % 
     Hypertension 46.1 47.3 
     Diabetes mellitus 24.3 23.9 
     Osteoarthritis 21.7 22.4 
     Cancer 3.5 4.1 
MMSE, M (SD) score 25.1 (1.7) 25.8 (0.6) 
ADLs, M (SD3.5 (1.6) 3.3 (1.8) 
Health services utilization 
 ER visits in past twelve   months, M (SD0.8 (0.8) 0.7 (9.3) 
 Hospital days in past   12 months, M (SD13.9 (9.9) 14.3 (9.7) 
 Patient episodes of   homecare service in past   twelve months, M (SD1.9 (1.2) 2.0 (1.2) 

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